SLAS 2026 & bio conference boston 2026: Ultimate Guide to Boost Drug R&D Efficiency

I. On the Eve of Transformation: Why 2026 Is the “Singularity” of Drug Discovery – And Why bio conference boston 2026 Takes Center Stage?

 1.1 From “Serendipitous Discovery” to “Designed Discovery”

 At bio conference boston 2026, we delve into drug discovery—a field pivotal to human health and well-being that has been shrouded in serendipity for over a century. In 1928, Fleming discovered penicillin in a contaminated petri dish, ushering in the antibiotic era; in the 1980s, researchers unexpectedly discovered sildenafil’s efficacy for male erectile dysfunction while screening antihypertensive drugs, leading to the blockbuster drug “Viagra.” Such cases are not uncommon in drug development history, reflecting the passivity of traditional drug discovery models—relying on large-scale compound screening, researchers’ “flashes of insight,” and unpredictable “luck.”

 Compared to the ‘brute-force battle’ I experienced when I entered the field in 2010—where eight pipetting workstations operating in three shifts could only process 5,000 compounds per day—the laboratories of 2026 have completed their evolution from ‘manual labor’ to ‘computational labor’. We no longer discuss manual sample loading; instead, we focus on how to complete a comprehensive evaluation of 100,000 compounds within 72 hours using a closed-loop system. To accelerate progress, eight microplate readers ran 24/7 with 12 technicians working in three shifts. Even so, the entire screening process took a full three months.More discouraging still, after three months we identified only three potential active compounds. Two were quickly eliminated due to excessive cytotoxicity, leaving just one. However, this final candidate was ultimately abandoned during subsequent optimization due to poor metabolic stability.

 This “needle in a haystack” screening approach not only consumes vast amounts of time and resources but also traps drug development in a high-investment, low-return trap. According to 2015 data from the Pharmaceutical Research and Manufacturers of America (PhRMA), it takes an average of 10-12 years for a new drug to progress from early discovery to market launch, with development costs reaching $2.8 billion and a clinical success rate hovering around 10%.In particular, inefficiency in the early compound screening phase is a core factor prolonging development cycles and driving up costs. Traditional screening not only struggles to accurately target drug targets but also often overlooks critical indicators like pharmacokinetic properties and toxicity due to its single-criterion approach, leading to numerous failures in later stages.

 By 2026, the deep integration of AI and automation technology has completely transformed this landscape.Last year, I visited Recursion Pharmaceuticals’ autonomous lab in Utah and witnessed the miracle of “screening 100,000 compounds in just three days.” This facility replaces rows of technicians with a closed-loop system—coordinated by AI—comprising robotic arms, high-throughput screeners, mass spectrometers, and other equipment, operating through a cycle of “data collection → model training → experiment design → result feedback.”

 Specifically, the process unfolds as follows: First, the AI model pre-screens 100,000 candidate compounds based on target structure, known structure-activity relationships (SARs), and historical screening data. It eliminates compounds with over a 90% probability of being inactive, narrowing the field to approximately 10,000 candidates.Next, the AI system instructs robotic arms to automatically perform compound aliquoting, dilution, and sample loading while simultaneously controlling high-throughput screening instruments for multidimensional detection. This encompasses not only target binding activity but also concurrent assessment of six critical metrics including cytotoxicity and metabolic stability.Test data is transmitted in real-time to the AI model, which analyzes it using reinforcement learning algorithms to identify structural features of active compounds. It then immediately designs the next screening round, adjusting parameters (such as compound concentration and detection timepoints), and directs robotic arms to initiate subsequent experiments.The entire process requires no human intervention. Full-dimensional screening of 100,000 compounds was completed in just 72 hours, ultimately identifying 12 highly active, low-toxicity candidate compounds. Eight of these advanced to subsequent optimization phases, with two currently progressing to Phase I clinical trials.

 To illustrate this transformation more clearly, I’ve summarized the core differences between traditional screening and AI-powered automated screening:

Comparison Dimensions	Traditional Screening Model (2010s)	AI + Automated Screening Model (2026)	Scale of Transformation
Screening Scale (100,000 compounds)	3 months (720 hours)	3 days (72 hours)	10x Efficiency Increase
Manpower Requirements	12 technicians working in three shifts	1 engineer responsible for system maintenance	Labor cost reduction: 92%
Testing Dimensions	Target binding activity only (1-2 indicators)	6 indicators including activity, toxicity, and metabolic stability	Testing dimensions increased by 3-6 times
Lead compound hit rate	0.003% (3/100,000)	0.012% (12/100,000)	Hit rate increased by 4 times
Proportion Advancing to Clinical Phase	0.00001% (1 in 100,000 ultimately enters clinical trials)	0.00002% (2/100,000 enter Phase I clinical trials)	Clinical advancement rate increased by 2 times
Single compound screening cost	$230	$35	Cost reduction of 85%

 The core of this transformation extends far beyond mere “speed gains.” It represents a paradigm shift from “passive screening” to “active design.” Traditional screening resembles “casting a blind net,” while AI-powered automated screening is “precision fishing.” AI models no longer merely process data; they deeply engage in the entire experimental design process. Through closed-loop iteration, they continuously optimize screening strategies and can even predict potential optimization directions for compounds.For instance, in Recursion’s labs, after identifying active compounds, AI models automatically generate structural optimization suggestions—such as “introducing a fluorine atom on the benzene ring enhances metabolic stability” or “replacing the ester group with an amide bond reduces toxicity.” The chemistry team then synthesizes compounds based on these recommendations, drastically shortening the lead compound optimization cycle.

 More importantly, this approach breaks down the “information silos” between drug discovery stages. In traditional R&D, screening, chemistry, and pharmacology teams operate in silos, leading to data transmission delays and biases. Within AI-driven systems, screening, synthesis, and pharmacology data synchronize in real-time to a unified platform. AI models integrate multidimensional information for holistic analysis, preventing decision errors confined to single stages.For instance, a compound demonstrating strong activity in target screening might be terminated early if AI models predict rapid metabolic inactivation based on its metabolic and toxicity data, thereby preventing unnecessary downstream investment.

 This shift from “serendipitous discovery” to “designed discovery” is fundamentally reshaping the logic of drug development. According to Deloitte’s 2025 Global Drug Development Trends Report, pharmaceutical companies adopting AI-powered automated screening reduce early-stage R&D cycles by an average of 40%, cut development costs by 35%, and boost clinical success rates to 18%.This explains why 2026 is hailed as the “singularity” of drug discovery—technological breakthroughs are no longer linear advancements but catalyze disruptive transformations, evolving drug development from a luck-dependent “gamble” into a predictable, engineered process.

 As a practitioner with 15 years in the industry, I keenly feel the impact of this transformation. Previously, we worried before project launches whether we could “screen for active compounds”; now, we focus more on “how to optimize screening strategies through AI models for more precise results.”This shift in mindset stems from the confidence technology has instilled. SLAS2026 serves as the premier showcase for this transformation—here, you’ll witness the world’s most cutting-edge autonomous lab technologies, engage in deep discussions with AI engineers, biologists, and chemists, and witness firsthand the dawn of the “designed discovery” era.

 1.2 SLAS2026’s Positioning: The “CES” of Life Sciences

 If you ask life science professionals what industry event is most worth attending in 2026,The answer would almost certainly be SLAS2026. Launched in 1990 as the Society for Laboratory Automation and Screening Annual Meeting, this conference initially focused on automating laboratory equipment. Yet after 36 years of evolution, SLAS has transcended its origins as a mere equipment exhibition to become a landmark event for cross-disciplinary convergence in life sciences—earning its reputation as the “CES of the life sciences.”

 For those unfamiliar with CES (Consumer Electronics Show), this analogy may seem abstract. Yet attending both events reveals their core logic aligns perfectly: CES serves as the “innovation crucible” for consumer electronics, bringing together hardware manufacturers, software developers, content creators, tech giants, and startups to catalyze life-changing technologies like smartphones, smart homes, and virtual reality.SLAS2026, meanwhile, serves as the “innovation connector” for life sciences, breaking down barriers between biologists, chemists, AI engineers, equipment manufacturers, and pharmaceutical R&D leaders to spark cross-disciplinary innovation.

 To grasp SLAS2026’s cross-disciplinary nature, we can trace the evolution of its attendees. When I first attended SLAS in 2010, participants were primarily lab managers, equipment procurement specialists, and R&D personnel from traditional pharmaceutical companies. Discussions centered on practical applications like “enhancing screening efficiency” and “operating automation equipment.”By 2026, SLAS’s attendee profile has undergone a dramatic transformation: According to pre-registration data from the conference website, this year’s participants include AI engineers, whose share has surged from under 5% in 2010 to 32%.with technology companies (including AI startups and life science divisions of internet giants) accounting for 28%. Interdisciplinary researchers (such as those with backgrounds in physics, computer science, or materials science working in life sciences) now represent 15%, while R&D personnel from traditional pharmaceutical companies have declined to 25%.

 This shift in attendee demographics directly informs the conference agenda. SLAS 2026’s main themes are organized into five pillars: “AI-Driven Drug Discovery,” “High-Throughput Bioanalytical Technologies,” “Laboratory Digital Transformation,” “Cross-Boundary Technology Integration,” and “Sustainable R&D Solutions.”The “AI-Driven Drug Discovery” segment occupies 28% of the agenda, covering cutting-edge topics like autonomous laboratories, generative AI compound design, and multi-omics data integration. Meanwhile, the “Cross-Boundary Technology Integration Applications” segment introduces interdisciplinary topics such as “Microfluidics + Machine Learning,” “Quantum Computing-Assisted Drug Design,” and “Synthetic Biology and Automated Screening” for the first time—areas that would have been unimaginable at SLAS a decade ago.Quantum Computing-Assisted Drug Design,” and “Synthetic Biology and Automated Screening”—topics that would have been unimaginable at SLAS a decade ago.

 To clearly illustrate the similarities and differences between SLAS and CES, as well as SLAS’s own evolution, I have compiled the following table:

Comparison Dimensions	SLAS 2026 (Life Sciences)	CES 2026 (Consumer Electronics)	SLAS 2010 (Historical Comparison)
Core Positioning	Cross-Industry Innovation Technology Exhibition and Exchange Platform	Consumer Electronics Innovation Showcase and Industry Matchmaking Platform	Laboratory Automation Equipment Exhibition
Attendee Profile	AI Engineers (32%), Pharmaceutical R&D (25%), Tech Companies (28%), Interdisciplinary Researchers (15%)	Hardware Manufacturers (35%), Software Developers (25%), Content Creators (18%), Investors (12%)	Lab Managers (40%), Equipment Procurement (30%), Pharmaceutical R&D (25%), Equipment Manufacturers (5%)
Core Topics	AI-Driven Drug Discovery, High-Throughput Screening, Laboratory Digitization, Cross-Boundary Technology Convergence	Artificial Intelligence, Virtual Reality, Smart Home, New Energy Technologies	Automated equipment operation, screening efficiency enhancement, equipment maintenance and calibration
Exhibition Highlights	Self-Driving Labs, AI Screening Platforms, Digital Laboratory Systems	Smart terminals, immersive experience devices, IoT solutions	Hardware equipment including high-throughput screening instruments, robotic arms, and microplate readers
Core Value	Cross-disciplinary technological integration and R&D efficiency enhancement	Consumer Experience Upgrades and Industrial Ecosystem Development	Equipment procurement and technology implementation
Purpose of Attendance	Technical Exchange (45%), Partnership Matching (30%), Trend Insights (20%), Equipment Procurement (5%)	Product Launch (35%), Partnership Negotiation (30%), Market Research (20%), Investment Matching (15%)	Equipment Procurement (60%), Technical Learning (30%), Peer Networking (10%)

 SLAS2026’s cross-disciplinary nature extends beyond its agenda and attendee demographics to its innovative conference formats and interactive experiences. This year marks the inaugural “Cross-Industry Innovation Lab” zone, featuring collaborative exhibition platforms by companies from diverse sectors including Google DeepMind, Illumina, Boston Dynamics, and Vertex Pharmaceuticals.For instance, Google DeepMind and Illumina collaborated to showcase an integrated “AI + Genomic Sequencing” solution: AI models rapidly analyze genomic sequencing data to identify disease-related targets, which are then validated using Illumina’s high-throughput sequencing equipment. This fully automated process eliminates manual intervention, significantly shortening the target discovery cycle.

 At last year’s SLAS conference, I witnessed a memorable cross-disciplinary dialogue: a microfluidics expert from Stanford University and a machine learning engineer from OpenAI engaged in an in-depth roundtable discussion on “how microfluidics technology can capture high-quality data to enhance AI model prediction accuracy.”The microfluidics expert argued that current AI prediction errors stem partly from experimental data noise (e.g., sample contamination, detection errors), while microfluidics enables single-cell precision manipulation to reduce such noise. The machine learning engineer countered that AI models could analyze microfluidic device operating parameters to predict potential experimental errors and proactively adjust algorithms for correction.This dialogue profoundly inspired many R&D personnel in attendance. Subsequently, multiple pharmaceutical companies’ R&D teams launched joint “microfluidics + AI” research projects based on these discussions.

The value of this cross-disciplinary collision lies at the heart of SLAS2026’s appeal. In today’s drug development landscape, increasingly reliant on multidisciplinary integration, breakthroughs within a single field alone can no longer drive transformative progress. For instance, developing ADCs (antibody-drug conjugates) demands the convergence of expertise in immunology, chemistry, pharmacology, and materials science. Similarly, screening nucleic acid therapeutics requires the coordinated application of gene editing technologies, bioinformatics, and automated detection techniques.SLAS2026 provides precisely such a platform where experts from different fields can “shake hands”—here, biologists can learn about the latest advances in AI technology, AI engineers can gain deep insights into the practical pain points of drug development, and equipment manufacturers can optimize product designs based on R&D needs. This ultimately fosters a virtuous cycle of “technological innovation → demand implementation → product iteration.”

 For attendees, this cross-disciplinary exchange broadens not only technical horizons but fundamentally reshapes R&D thinking.Previously, when tackling low screening efficiency, we often focused solely on “how to upgrade screening equipment.” Now, through discussions with AI engineers, we consider “how to optimize screening strategies through data-driven approaches.” Through exchanges with microfluidics experts, we explore “how to obtain more precise data through experimental technique innovation.” This shift in mindset often drives greater improvements in R&D efficiency than the application of any single technology alone.

 As the “CES of life sciences,” SLAS2026 transcends the scope of a conventional industry conference. It serves not only as a showcase for cutting-edge technologies but also as a bridge connecting talent, technology, and demand—a core engine driving the evolution of drug discovery from “single-technology application” to “multidisciplinary integration.”If you work in drug R&D—whether as a scientist focused on traditional pharmacology, an engineer specializing in AI, or a manager overseeing technology procurement—SLAS2026 offers an opportunity to break free from established boundaries and embrace cross-disciplinary innovation. Here, you may encounter future collaborators, discover solutions to long-standing R&D challenges, and even glimpse the next wave of transformation in drug discovery.

 1.3 In-Depth Guide: Beyond the Official Handbook

 If you’re planning to attend SLAS2026, you’ll likely do two things before departure: download the official conference handbook from the website and search online for attendee guides. Frankly, the official handbook functions more like a “conference manual,” listing basic details like schedules, exhibitor lists, and venue information—but it rarely tells you “which sessions are truly worth attending,” “how to quickly locate relevant resources among 300+ exhibitors,” or “how to avoid post-conference information overload.” Meanwhile, most online guides consist of fragmented, personal anecdotes lacking systematic structure or targeted advice, failing to meet the diverse needs of attendees at different career stages.

 This is precisely why I wrote this guide—as a seasoned SLAS attendee who’s been to 15 consecutive conferences. From my early days as a newbie to my current role leading a team in early-stage drug discovery R&D, I’ve witnessed SLAS’s evolution firsthand and stumbled into countless pitfalls:I’ve been pinned down by salespeople in the exhibition hall for an hour without gaining any valuable technical insights; I’ve collected dozens of materials during conferences only to let them gather dust on my hard drive because I didn’t know how to implement them.

 The value of this article lies in helping you avoid these pitfalls I’ve encountered. It empowers you to navigate the intensive week of SLAS 2026 with minimal time investment and maximum efficiency, securing the most valuable information and resources. After reading this guide, you’ll save at least 40 hours of wasted time—including time spent sifting through the agenda, aimlessly wandering the exhibition hall, processing post-conference information overload, and later communicating with your boss about technology adoption.

 To help readers with different needs pinpoint relevant content, I’ve created a “Reader Value Matching Chart.” Use your role and conference objectives to quickly identify key sections:

Reader Profile	Core Attendance Goal	Key Sections to Read	Core Time Saved	Key Benefits
New Professionals (1-3 years in the field)	Quickly grasp industry trends, learn cutting-edge technologies, and build professional networks	I. On the Eve of Transformation, II. Technical Core, III. Value Benchmarking, IV. Master Classes	30 hours (10 hours for agenda curation, 12 hours for exhibition exploration, 8 hours for information consolidation)	Build systematic industry knowledge, master efficient conference participation techniques, cultivate high-quality professional connections
Senior R&D Professionals (5-10 years of experience)	Seek technical solutions, refresh knowledge, connect with collaborative resources	II. Technical Core, IV. Master Classes, V. Risk Mitigation & Follow-Up	45 hours (20 hours for technical research, 15 hours for partnership matching, 10 hours for report writing)	Identify solutions for R&D pain points, expand collaboration channels, enhance technology implementation efficiency
Technology Procurement/Management (8+ years of experience)	Evaluate technical feasibility, calculate return on investment, drive technology adoption	III. Value Benchmarking, V. Risk Mitigation and Follow-Up	50 hours (25 hours for technical evaluation, 15 hours for ROI analysis, 10 hours for reporting and communication)	Accurately assess technology value, rapidly drive technology adoption, secure executive buy-in
AI/Cross-Border Technology Practitioners	Understand life sciences sector needs, identify technology implementation scenarios	I. On the Eve of Transformation, II. Technical Core, IV. Masterclass Participation	35 hours (15 hours for needs assessment, 12 hours for scenario matching, 8 hours for networking)	Define technology application directions, identify potential clients, implement technical solutions

 Next, I’ll briefly break down this article’s core value to give you a preview of what you’ll gain from reading it:

 I. Precision Agenda Filtering Strategy to Avoid “Rushing Blindly from Session to Session”

 SLAS 2026 features an extremely packed schedule—over 200 formal presentations alone, plus workshops, roundtables, and exhibitor demos totaling more than 500 events. Without clear selection criteria, you risk falling into the trap of “wanting to attend every session but ending up not understanding any.”In Chapter 4, “Masterclass for Conference Attendees,” I’ll teach you a “pain point tagging” screening method: First, list your lab’s top three core R&D challenges (e.g., “Stability issues in ADC drug screening,” “Insufficient AI model prediction accuracy,” “Incompatible lab equipment data”). Then, use these “pain point tags” to filter relevant sessions via the conference website’s agenda search function.

 For instance, if you’re tackling “ADC drug screening stability issues,” search keywords like “ADC,” “stability screening,” and “high-throughput analysis.” This will uncover newly added 2026 sessions like “High-Throughput Stability Screening Technologies and Applications for ADC Drugs” and “Automated Systems in ADC Coupling Efficiency Detection.”Additionally, I’ll share techniques for identifying “high-quality sessions”—such as evaluating speaker credentials (verifying they are frontline R&D leaders rather than sales representatives), reviewing session descriptions (looking for concrete case studies instead of generic technical overviews), and checking historical feedback (using SLAS’s attendee community to assess past sessions with similar topics).

 II. Effective Networking Techniques to Make a Lasting Impression

 A core value of attending SLAS is connecting with industry leaders and potential partners. Yet many fall into two networking pitfalls: either failing to initiate conversations or opening with “I admire your work—can we exchange business cards?”which rarely leaves a lasting impression. In Chapter 4, I’ll share a “value-driven” networking approach: Before engaging with experts, thoroughly research their research focus or your company’s latest developments. Then, tie this to your specific R&D challenges by posing concrete questions or collaboration ideas.

 For example, if you wish to connect with the Chief Scientist of an AI drug discovery company, research their latest model release beforehand. During the conversation, you could say: “I understand your company’s XX model has demonstrated exceptional performance in kinase target screening. Our team is currently working on drug screening for EGFR mutants and has encountered significant discrepancies between model predictions and experimental results. I’d like to ask if your model employs any specialized optimization strategies when handling such mutated targets?”” Such an approach not only demonstrates your expertise but also sparks in-depth discussion, laying the groundwork for potential future collaboration. Additionally, I’ll share specific techniques for Q&A sessions, networking strategies for dinner events and coffee breaks, and other practical tips to help you build high-quality connections efficiently.

 III. High-Throughput Screening Guide for the Exhibition Hall: Avoiding Ineffective Communication

 The SLAS2026 exhibition hall features over 300 exhibitors spanning equipment manufacturers, AI technology providers, and CRO companies. Wandering aimlessly risks getting bogged down by sales pitches, wasting time without gaining valuable insights.In Chapter 4’s “Exhibition Hall Exploration Guide,” I’ll teach you how to quickly identify exhibitors “worth deep conversations.” First, assess whether their core technology aligns with your needs. Then, check if an Application Scientist is present at their booth (Application Scientists typically have hands-on R&D experience and can provide concrete technical solutions, not just product pitches).

 For example, when visiting an automation equipment manufacturer’s booth, skip the sales pitch on product specs and ask directly: “Do you have an Application Scientist here? I’d like to understand specific application cases for this equipment in nucleic acid drug screening—such as whether it can address stability testing challenges for RNA therapeutics?” Additionally, I’ll share how to prepare an “Exhibitor Communication Checklist”—pre-listing core questions you want answered (e.g., technical principles, application cases, cost estimates, after-sales service) to avoid missing critical information during discussions.

 IV. Practical Risk Mitigation and Implementation Methods to Maximize Conference Value

 Many attendees face two common challenges post-conference: first, information overload—collecting vast amounts of material without knowing how to organize it; second, difficulty in technology implementation—wanting to adopt new technologies but lacking management support.In Chapter 5, I’ll share a “Daily Decluttering” review method: spend 15 minutes each evening recording only the 3 most critical insights or takeaways, rather than writing tens of thousands of words in notes. This prevents information overload while enabling quick post-event review of core content.

 Additionally, I’ll teach you how to craft a “Technology Adoption Report” that translates conference insights into ROI analysis your boss cares about.For example: Calculate that introducing an AI screening platform costs $500,000, but is projected to: – Shorten early screening cycles by 6 months – Save $100,000 in monthly R&D costs – Increase clinical success rates by 5% – Generate $100 million in potential post-market drug revenue This yields an approximate ROI of 200%. Such data-driven analysis significantly boosts the approval rate for technology adoption.

 Additionally, I’ll share how to capture “inside information” from informal meetings—such as coffee break conversations or dinner discussions. These settings often reveal the latest industry developments (e.g., a pharmaceutical company’s R&D pipeline adjustments, breakthroughs in new technologies) that are difficult to obtain through formal agendas.

 Finally, I wish to emphasize that this article’s value lies not merely in offering “tips,” but in fostering a “conference mindset”—SLAS 2026 is not a passive information-gathering event, but an active value-mining endeavor. Only by arriving with clear objectives and mastering efficient methods can one emerge from this high-intensity gathering with substantial gains.

 Next, Chapter 2 will delve into the three critical technological inflection points dominating drug discovery in 2026: AI-driven autonomous labs, high-throughput precision screening for ADCs and nucleic acid therapeutics, and the standardization of lab operating systems alongside API culture. If you’re a tech enthusiast or seeking solutions to R&D pain points, this chapter will be your focal point. Now, let’s dive into the heart of this technological revolution and explore the future trajectory of drug discovery.

II. Tech Core: Three Critical Technology Inflection Points to Watch in 2026 at bio conference boston 2026

 2.1 AI-Driven “Self-Driving Labs” (SDLs)

 In the technological revolution of drug discovery, Many mistakenly believe SDL is simply ‘robots + AI’—that’s an outdated concept from 2020. At SLAS 2026, you’ll witness the true essence of Self-Driving Labs: it doesn’t execute pre-programmed routines, but autonomously determines the next dilution gradient based on mass spectrometry feedback from the previous round. This ‘driverless’ drug design represents the ultimate solution for reducing R&D costs and boosting efficiency, capable of autonomously completing the entire cycle: “data collection → model training → experiment design → result feedback → solution refinement.”By 2026, this system has evolved from laboratory prototypes to large-scale implementation. Its core breakthrough lies in AI no longer functioning as a mere data processing tool, but as a “decision-making brain” deeply embedded throughout the entire experimental workflow. It can directly command robotic arms and high-throughput equipment to execute the next round of targeted experiments, achieving exponential improvements in R&D efficiency.

 I. The Core Logic of Closed-Loop: From “Passive Analysis” to “Active Decision-Making”

 The closed-loop system of the self-driven laboratory fundamentally mimics the R&D mindset of human scientists while executing iterations with machine precision and speed. Its core process can be broken down into four key stages, forming a seamlessly interconnected cycle:

 1. Data Acquisition Layer: A Comprehensive, All-Angle “Perception System”

 Traditional labs rely on manual logging or isolated device outputs, resulting in severe data fragmentation (e.g., screening data confined to microplate readers, metabolic data stored in mass spectrometry systems, with no real-time integration). The self-driving lab’s collection layer achieves real-time data interoperability between “devices-software-models” through embedded sensors and standardized data interfaces.

 Take Recursion Pharmaceuticals’ SDL system as an example. Their laboratory is equipped with over 500 embedded sensors covering every stage from compound dispensing and cell culture to detection and analysis: precision of robotic arm pipetting volumes (error ≤0.1μL), temperature fluctuations in incubators (±0.05℃), signal intensity of detection equipment, and even environmental humidity and atmospheric pressure data—all transmitted in real-time to a central database.This data encompasses not only experimental outcomes (e.g., compound activity values) but also “environmental variables” during the process—factors often overlooked in traditional R&D yet critical to experimental reproducibility.

 2. AI Model Layer: The “Decision Brain” of Reinforcement Learning + Generative AI

 The core driving force of the closed-loop system is the AI model, whose core technology combination is “Reinforcement Learning (RL) + Generative AI.” Reinforcement Learning dynamically adjusts strategies based on experimental results, while Generative AI designs novel experimental protocols or compound structures.

 Unlike traditional machine learning models that “predict but do not decide,” the AI models in self-driving labs incorporate a “reward mechanism”: When experimental outcomes meet expectations (e.g., identifying highly active compounds), the model reinforces the current screening strategy. When results are suboptimal (e.g., compounds exceeding toxicity thresholds), the model automatically analyzes failure causes (Is it a target binding issue? Or insufficient metabolic stability?) and adjusts parameters for the next round of experiments.

 For instance, in Insilico Medicine’s “AI Chemist” system, three initial compounds screened for fibrosis targets were all rejected due to excessive liver toxicity. By analyzing the structural features of these compounds and correlating their metabolic and toxicity data, the AI model identified the “benzothiophene structure + ester side chain” as the key motif causing hepatotoxicity.Subsequently, generative AI automatically designed 12 new compound structures with substituted side chains (replacing the ester group with amide or sulfonamide bonds). Using reinforcement learning algorithms for prioritization, it selected five compounds with “predicted metabolic stability ≥85%” and “toxicity risk score ≤0.3.” The robotic arm was then instructed to initiate the next round of synthesis and screening—the entire decision-making process took only 4 hours, whereas such structural optimization and protocol adjustments would typically require at least 2 weeks in traditional R&D.

 3. Experimental Execution Layer: Highly Automated “Execution Limbs”

 The experiment execution layer serves as the closed-loop “implementation end,” centered around an “unmanned operation system” comprising robotic arms, high-throughput workstations, automated incubators, and other equipment. Unlike traditional automated devices designed for “single-task execution,” the execution layer of self-driven laboratories possesses “multi-task coordination” and “real-time response” capabilities—dynamically adjusting experimental workflows based on AI model instructions without human intervention.

 Take the “Atlas Lab System” developed by Boston Dynamics and Agilent as an example. Its core is the Atlas robotic arm equipped with an AI scheduling module, integrated with Agilent’s high-throughput screening workstation:

•  When the AI model instructs “Prioritize cytotoxicity testing for compound A,” the robotic arm automatically reorders tasks, pausing the current activity screening to switch to toxicity testing protocols;
•  Upon detecting that compound A’s toxicity exceeds thresholds, the AI model instantly issues a command to “halt further experiments with compound A and initiate compound B synthesis.” The robotic arm immediately coordinates with the synthesis workstation to automatically perform compound B’s aliquoting, reaction, and purification.

 This system achieves response delays ≤10 seconds, completes up to 8 experimental iterations per day (compared to 1-2 in traditional labs), and delivers 99.7% experimental reproducibility (vs. ~85% for manual operations).

 4. Feedback Regulation Layer: The Real-Time Iterative “Closed-Loop Hub”

The feedback regulation layer serves as the core connector between “data-model-experiment.” Its function is to compare experimental results with AI model predictions in real time, calculate errors, and drive model optimization.For example, if an AI model predicts compound C’s target binding activity with an IC50 ≤ 10 nM, but actual testing yields IC50 = 45 nM, the feedback layer automatically analyzes the cause of discrepancy: Is it insufficient feature extraction by the model? Or interference from environmental variables during the experiment?

 If the issue lies with the model, the feedback layer supplements the training set with the compound’s structure and experimental data, then retrains the model. If environmental variables are the cause (e.g., incubator temperature fluctuations exceeding thresholds), it automatically adjusts equipment parameters and increases temperature monitoring sampling frequency in subsequent experiments. This “real-time feedback-rapid correction” mechanism enables the AI model and experimental system to co-evolve, becoming increasingly accurate with use.

 To illustrate the closed-loop advantages of the self-driving lab more intuitively, I’ve compiled a comparison of core differences between traditional labs and the SDL system:

Comparison Dimensions	Traditional Laboratory (2020s)	Self-Driven Laboratory (2026)	Technological Breakthrough
Experiment Iteration Speed	1–2 rounds/day	8 rounds/day	Multi-task coordination + real-time response, boosting iteration efficiency by 4-8 times
Data Collection Scope	Experiment outcome data only (activity, toxicity, etc.)	Result data + process data (environmental, equipment parameters, etc.)	Embedded sensors + standardized interfaces, increasing data dimensions by 10 times
AI Roles	Post-event data analysis tools	Real-time decision-making command system	Reinforcement learning + generative AI, with proactive optimization capabilities
Experiment Reproducibility	Approximately 85%	99.7%	Unmanned Operation + Precise Control of Environmental Variables
Manpower requirements	5-8 personnel per experimental cycle	0.5 person (system maintenance only)	Multi-device collaborative scheduling reduces labor costs by 90%
Compound optimization cycle	3–6 months	2–4 weeks	Closed-loop iteration + AI design, boosting optimization efficiency by 5-8 times
R&D failure rate	Approximately 70% in early stages	Approximately 35% in early stages	Real-time feedback adjustments eliminate ineffective compounds early

 II. SLAS2026’s Core SDL Showcase: Practical Case Studies from Prototype to Implementation

 SLAS2026 serves as a central showcase for self-driving laboratory technology. This year’s exhibition featured over 20 companies and research institutions presenting SDL system implementation cases, with three key areas deserving special attention:

 1. SDL in Small-Molecule Drug Discovery: Recursion’s “AI + Cell Phenotyping” Closed-Loop Approach

 Recursion demonstrated its SDL system’s application in drug discovery for idiopathic pulmonary fibrosis (IPF):

•  Project Objective: Screen small-molecule compounds that inhibit fibroblast activation;
•  Closed-loop process: AI models pre-screened 2,000 potential molecules from a 1-million-compound library, directing the SDL system to perform cellular phenotype screening → 32 active compounds were identified and fed back to the AI model → The model analyzed their action pathways, generating 150 new structural analogues → The SDL system automatically synthesized and screened these, yielding 8 highly active molecules → After further optimization, 2 molecules advanced to preclinical studies.The entire process took only 11 weeks (compared to 10-12 months in traditional R&D).

 More notably, Recursion unveiled its “SDL + Multi-omics” integrated approach for the first time—integrating single-cell RNA sequencing and proteomics data into a closed-loop system in real time. The AI model can determine compound mechanisms based on multi-omics data, avoiding the R&D pitfall of “high activity but unclear mechanism.”

 2. SDL Breakthrough in Synthetic Biology: Ginkgo Bioworks’ “Biofoundry”

 Ginkgo Bioworks’ “Biofoundry SDL” integrates autonomous laboratories with synthetic biology, focusing on efficient microbial strain optimization:

•  Traditional strain optimization: Manually screening 1,000 strains after CRISPR editing takes 4-6 weeks with a success rate of approximately 5%.
•  SDL Optimization Workflow: AI model designs 10,000 CRISPR editing strategies → SDL system automatically completes strain construction, cultivation, and metabolite detection → Real-time analysis of yield data feeds back to AI model → Model adjusts editing targets and initiates next screening round → Completes 5 iterative rounds within 7 days, selecting strains with 30x yield increase and 22% success rate.

 The core innovation of this system lies in its “modular design”—users can replace “editing modules,” “cultivation modules,” and “detection modules” according to their needs, adapting to R&D requirements for different microorganisms (bacteria, yeast, fungi). It is currently used by pharmaceutical companies such as Merck and Bayer for large-scale production of biopharmaceutical raw materials.

 3. Open-Source SDL Solutions for Academic Institutions: MIT’s “OpenSDL”

 MIT unveiled its open-source autonomous lab framework “OpenSDL” at the exhibition, aiming to lower adoption barriers for small-to-medium pharmaceutical companies and research institutions. The framework includes:

•  Hardware interface standards: Supports integration with robotic arms and screening instruments from major brands (e.g., Agilent, Thermo, Beckman)
•  Open-source AI model library: Provides foundational code for reinforcement learning and generative AI, enabling users to customize solutions;
•  Data standardization tools: Supports industry-standard formats like ISA-TAB and OMEX for seamless cross-platform data sharing.

 Currently, 12 startup pharmaceutical companies have adopted OpenSDL to build small-scale autonomous labs, reducing R&D costs by 60% and shortening early-stage screening cycles by 70%. This signifies that autonomous labs are no longer exclusive tools for pharmaceutical giants but are becoming mainstream across the industry.

 III. Technical Challenges and Industry Trends for SDL: Core Issues to Watch in 2026

 Despite rapid advancements, autonomous labs face three major technical challenges, which were key discussion points at SLAS 2026:

 1. Data Quality and Annotation Challenges: “Garbage In, Garbage Out”

 The effectiveness of closed-loop systems heavily relies on data quality, yet current lab data suffers from inconsistencies in annotation and excessive noise. For instance, different testing devices define “positive activity” differently (some consider IC50 ≤ 10 nM as positive, others IC50 ≤ 20 nM), rendering data unusable for direct AI model training.

 Solution: The industry is advancing the “Experimental Data Labeling Standard (EDLS),” developed by SLAS in collaboration with the FDA and PhRMA. This standard establishes clear annotation rules for experimental data (e.g., threshold definitions for target activity, toxicity, and metabolic stability). By 2026, the standard had been piloted at 15 leading pharmaceutical companies, with full-scale implementation expected in 2027.

 2. Multimodal Data Fusion Bottleneck: Integrating “Wet Lab + Dry Lab” Data

 Self-driving laboratories require integrating “wet lab data” (e.g., cell activity, metabolite concentrations) with “dry lab data” (e.g., target structures, molecular dynamics simulation results). However, the vast differences in format and dimensionality between these two data types make direct fusion challenging.

 Current breakthrough direction: Google DeepMind’s “AlphaFold-Lab” model enables real-time correlation between AI-predicted target structure data and wet lab screening data, extracting shared features through attention mechanisms. During a live demonstration at SLAS, this model analyzed conformational changes in target structures to predict compound binding patterns, boosting screening hit rates by 2.3 times.

 3. Insufficient System Flexibility: Difficulty Adapting to Complex Experimental Scenarios

 Existing SDL systems primarily target standardized experiments (e.g., small molecule screening, strain optimization) and lack flexibility for complex scenarios (e.g., ADC drug conjugation efficiency testing, cell therapy process optimization). This is because such scenarios involve more intricate workflows and variables that cannot be adequately covered by fixed modules.

 Countermeasure: Combining Modularity with Programmability. For instance, Thermo Fisher’s “FlexSDL” system enables users to customize experimental workflows via a visual programming interface (e.g., adding “ADC Coupling Efficiency Detection” or “CAR-T Cell Expansion Monitoring” modules) without modifying underlying code, thereby adapting to diverse R&D scenarios.

 IV. SDL’s Practical Value in Drug Development: Comprehensive Transformation from Cost and Efficiency to Success Rates

 For R&D leaders, SDL’s core value extends beyond “technological novelty” to tangible benefits directly translating into commercial returns. According to Deloitte’s 2026 Self-Driven Laboratory Industry Report, pharmaceutical companies adopting SDL systems achieved significant improvements across three core metrics:

Core Metrics	Pharmaceutical Companies Not Using SDL	Pharmaceutical Companies Adopting SDL	Improvement Rate
Early R&D Cycle (Small Molecule Drugs)	18–24 months	4–6 months	75%-83%
Early R&D Costs	$15-20 million	$4-6 million	67%-80%
Preclinical Candidate Compound (PCC) Hit Rate	5%-8%	18%-22%	2.25–4.4 times
Phase I clinical trial success rate	12%-15%	25%-28%	1.67–2.33 times
R&D Team Size	30-40 people	10–15 people	50%-75%

 Consider a real-world example from a mid-sized pharmaceutical company: In 2024, the company adopted the SDL system for early discovery of small-molecule drugs in oncology. In 2023 (without SDL), its oncology pipeline included 3 projects entering preclinical research, with cumulative R&D investment

Reaching £18 million, ultimately only one project progressed to Phase I clinical trials; 2025 (post-SDL implementation), with the same £18 million investment. The company completed early development for five projects, three of which entered Phase I clinical trials, achieving a Phase I success rate of 27% (industry average: 15%).

 Additionally, SDL delivered “hidden value”—enhanced precision in R&D decision-making. In traditional development, advancing a compound often relied on scientists’ experience-based judgments. The SDL system, however, provides quantitative “continue/terminate” recommendations through multidimensional data analysis, preventing ineffective investments caused by experiential biases.For example, a compound demonstrated excellent activity in screening but was predicted by the SDL system to undergo rapid metabolic inactivation in vivo based on its metabolic and toxicity data. This led to the early termination of its development, saving $3 million in preclinical optimization costs.

 V. SLAS2026 Attendee Guide: How to Deepen Your Understanding of SDL Technology?

 To gain comprehensive insights into the latest SDL advancements at SLAS2026, focus on these three key areas:

 1. Core Agenda Recommendations

•  Closed-Loop Design for Self-Driving Labs: From Prototype to Scalable Deployment (Speaker: Jeremy Wertheimer, CTO of Recursion): Deep dive into SDL’s technical architecture and implementation challenges;
•  “Multimodal Data Fusion: The Next Technical Inflection Point for SDL” (Speaker: Alex Bateman, Senior Researcher, Google DeepMind): Explores how AI models integrate wet and dry lab data;
•  “Open-Source SDL: A Technical Breakthrough Path for Small and Medium-Sized Pharma Companies” (Speaker: MIT Professor Timothy Lu): Introduces the usage methods and case studies of the OpenSDL framework.

 2. Featured Exhibitors in the Exhibition Hall

•  Recursion: Live demonstration of SDL system real-time operation with interactive experimental design experience;
•  Ginkgo Bioworks: Showcasing SDL applications in synthetic biology with hands-on strain optimization case studies;
•  Thermo Fisher: Introducing trial plans for the FlexSDL system with on-site customizable experimental workflows;
•  MIT OpenSDL Booth: Technical support for the open-source framework, with detailed deployment guides available for pickup.

 3. Workshop Participation

 SLAS2026 debuts the “Hands-On SDL System Setup Workshop” featuring step-by-step instruction from industry experts:

•  Day 1: SDL Hardware Selection and Interface Adaptation (connecting devices from different brands);
•  Day 2: AI Model Secondary Development (How to adjust model parameters based on specific R&D needs);
•  Day 3: Data Standardization and Compliance (Meeting FDA Electronic Data Integrity Requirements).

 Workshop capacity is limited to 50 participants. Advance registration is required via the official website. Attendees will receive an SDL system practical manual and open-source code package—an indispensable “hands-on course” for enterprises planning to build SDL systems.

 Summary: The Core Value of Self-Driven Labs—Making R&D “Predictable, Reproducible, and Accelerated”

 By 2026, the widespread adoption of Self-Driven Laboratories is transforming drug R&D from an “art reliant on scientist expertise” into an “engineering process grounded in data and models.” Its core breakthrough lies in “closed-loop” integration—the deep fusion of AI and automated equipment enables autonomous iteration of R&D workflows, making drug discovery timelines, costs, and success rates predictable and reproducible.

 For attendees at SLAS 2026, understanding SDL technology is not merely about “tracking trends” but seizing opportunities. Whether you’re an R&D leader seeking technical solutions, an AI engineer exploring implementation scenarios, or a manager planning technology adoption, this technical showcase offers resources tailored to your needs. As Recursion CEO Chris Gibson stated in his SLAS opening address, the ultimate value of SDL lies in:We are not replacing scientists, but liberating them from repetitive tasks so they can focus on more creative decision-making—that is the essence of technological revolution.”

 2.2 High-Throughput Precision Screening for ADCs and Nucleic Acid Therapeutics

 Antibody-drug conjugates (ADCs) and nucleic acid therapeutics (including siRNA, mRNA, ASO, etc.) represent two of the hottest tracks in current drug development. According to PhRMA data from Q1 2026, 187 ADC drugs and 232 nucleic acid therapeutics are in global development, collectively accounting for 35% of the innovative drug pipeline.However, developing these drugs is far more challenging than traditional small molecules: ADCs face stability-versus-specificity trade-offs in their “antibody-payload-linker” triad system, while nucleic acid therapeutics grapple with in vivo degradation and inefficient targeted delivery.Traditional screening methods (such as manual screening and low-throughput testing) struggle to address their complex development requirements. By 2026, high-throughput “precision screening” technology—through the deep integration of automation, microfluidics, and AI—is precisely resolving these core challenges.

 I. Development Challenges of ADC Drugs and High-Throughput Precision Screening Solutions

 The core of ADC drug development lies in “precisely targeting tumor cells,” with technical bottlenecks concentrated in three dimensions: uniformity of conjugation efficiency (impacting efficacy and safety), linker stability (preventing off-target toxicity from payload detachment in circulation), and antibody tumor specificity (minimizing damage to normal cells).Traditional screening methods rely on manual operations, capable of testing only 1-2 indicators at a time, and struggle to achieve single-cell precision analysis. This results in low screening efficiency and a hit rate below 5%.

 By 2026, high-throughput precision screening technology will achieve “precision, efficiency, and comprehensive coverage” in ADC screening through the integration of “automated conjugation platforms + multidimensional detection systems + AI data analysis.”

 1. Automated Coupling Efficiency Screening: Addressing the “Homogeneity” Challenge

 ADC conjugation efficiency (Drug-to-Antibody Ratio, DAR) is a critical determinant of therapeutic efficacy—ideal ADCs require uniform DAR (e.g., DAR=4). Traditional conjugation processes yield dispersed DAR distributions (e.g., DAR=2-8), necessitating manual separation and purification, which is time-consuming and inefficient.

 By 2026, mainstream “automated conjugation-screening integrated platforms” (e.g., Agilent’s ADC PureSelect System) will achieve seamless integration of conjugation reactions and DAR detection:

•  Automated conjugation: Robotic arms precisely dispense antibodies, payloads, and linkers (error ≤0.01 mg/mL) while controlling reaction temperature (±0.1°C) and pH (±0.05), ensuring consistent conjugation outcomes;
•  Real-time DAR monitoring: The platform integrates ultra-performance liquid chromatography (UPLC) with mass spectrometry (MS). Samples are automatically collected every 30 minutes during coupling to detect DAR distribution, generating real-time curves;
•  AI-Optimized Coupling Parameters: AI models automatically adjust reaction conditions (e.g., increasing linker concentration, extending reaction time) based on DAR distribution data to ensure final DAR uniformity ≥90% (traditional processes achieve ~60%-70% uniformity).

 Example: ADC Development Targeting HER2 at a Pharmaceutical Company: Traditional process: Manual coupling screening required 5 rounds (3 days per round), yielding 1 DAR-uniform ADC candidate after 15 days.After adopting an automated conjugation platform, only one round of 24 hours is required to screen out three candidates with DAR uniformity ≥92%, achieving a 15-fold increase in efficiency and a 65% reduction in screening costs.

 2. High-Throughput Linker Stability Testing: Solving the “Off-Target Toxicity” Challenge

 Linker stability directly impacts ADC safety—it must remain stable in circulation (preventing payload detachment) and rapidly release the payload upon entering tumor cells. Traditional stability testing relies on “in vitro incubation + manual sampling,” limiting detection to one linker at a time and failing to simulate complex in vivo conditions (e.g., varying pH levels, enzyme concentrations), potentially rendering selected linkers ineffective in vivo.

 The 2026 “Multi-Environment Simulated Stability Screening System” (e.g., PerkinElmer’s LinkerStable HT) integrates microfluidic chips with automated detection technology to achieve high-throughput, multi-condition screening of linker stability:

•  Microfluidic chips simulate in vivo environments: The chip integrates six distinct microenvironment channels (pH 5.0–7.4, varying concentrations of lysosomal enzymes, serum proteins), simultaneously mimicking conditions within tumor cells, blood circulation, and normal tissues.
•  High-throughput parallel detection: Simultaneously analyzes 96 linkers per run, concurrently capturing stability data across all six microenvironments, reducing the testing cycle from 7 days to 12 hours;
•  Real-time release kinetics analysis: Utilizing fluorescence resonance energy transfer (FRET) technology, the system monitors payload release rates in real time, generating kinetic curves. An AI model automatically calculates “circulatory stability scores” and “tumor cell release efficiency scores,” identifying optimal conjugates with “high circulatory stability + high tumor release efficiency.”

 During the live demonstration at SLAS2026, the system successfully screened one linker for an EGFR-targeted ADC: it exhibited 98% stability at pH 7.4 (circulatory environment), with only 2% payload detachment within 24 hours,and achieved 95% release efficiency at pH 5.5 + lysosomal enzymes (tumor intracellular environment), releasing 95% of the payload within 6 hours. This significantly outperforms traditional linkers (circulatory stability: 85%, release efficiency: 70%).

 3. Tumor-Specific Single-Cell Screening: Enhancing “Targeting Precision”

 The antibody component of ADCs must exhibit high tumor specificity to avoid off-target toxicity caused by binding to normal cells. Traditional screening methods rely on “cell population detection,” which cannot distinguish binding differences at the single-cell level and may overlook low-abundance but highly specific antibodies.

 The 2026 “single-cell level ADC specificity screening platform” (e.g., Single-Cell ADC Select developed by 10x Genomics and Bio-Rad) achieves precise screening through the integration of single-cell sequencing and flow cytometry:

•  Single-cell capture and staining: An automated system mixes tumor cells with normal cells (1:100 ratio), captures individual cells via microfluidic chips, and stains them with fluorescently labeled ADC candidates;
•  Multiparameter Analysis: Flow cytometry simultaneously detects each cell’s “ADC binding intensity,” “cell type markers,” and “apoptosis signals,” identifying ADCs that “bind exclusively to tumor cells and induce apoptosis”;
•  Single-Cell Sequencing Validation: Selected positive cells undergo single-cell RNA sequencing to confirm expression of tumor-specific markers, eliminating false positives.

 This platform achieves a screening throughput of 100,000 cells per hour, with specificity hit rates increasing from 8% in traditional methods to 25%. In an ADC development project targeting triple-negative breast cancer, an antibody selected via this platform demonstrated an 89% tumor inhibition rate in preclinical animal studies, while off-target toxicity (e.g., cardiotoxicity, hepatotoxicity) was reduced by 70% compared to conventional ADCs.

 To clearly illustrate the evolution of ADC screening technology, I have compiled a comparison between traditional methods and the 2026 high-throughput precision screening approach:

Screening Dimension	Traditional Screening Methods	2026 High-Throughput Precision Screening Method	Core Advantages
Coupling Efficiency (DAR)	Manual Coupling + Offline UPLC Detection, DAR Uniformity 60%-70%	Automated coupling + real-time UPLC-MS detection, DAR uniformity ≥90%	15x efficiency improvement with significantly enhanced uniformity
Coupling Agent Stability	Single-environment, low-throughput detection, 7-day cycle	Parallel detection across 6 microenvironments, 12-hour cycle	Comprehensive coverage of complex in vivo environments, 14-fold increase in screening efficiency
Tumor specificity	Cell cluster detection, 8% specificity hit rate	Single-cell detection + RNA sequencing validation, 25% hit rate	Avoids false positives, reduces off-target toxicity risk
Detection Metrics	1-2 indicators per run	Simultaneous assessment of 6 metrics including conjugation efficiency, stability, and specificity	Comprehensive evaluation of ADC performance
Screening cost (per 100 candidates)	$250,000	$85,000	Cost reduction of 66%
Preclinical entry rate	3%-5%	15%-20%	R&D success rate increased by 3-4 times

 II. R&D Challenges in Nucleic Acid Therapeutics and High-Throughput Precision Screening Solutions

 The core challenge for nucleic acid therapeutics (siRNA, mRNA, ASO) is “in vivo delivery efficiency”—nucleic acid molecules are prone to degradation by nucleases and struggle to penetrate cell membranes, necessitating delivery systems (e.g., lipid nanoparticles LNP, viral vectors, targeted ligand conjugates) for targeted delivery.Additionally, nucleic acid therapeutics face challenges such as immunogenicity (triggering immune responses) and off-target effects (silencing non-target genes). Traditional screening methods rely on animal testing or low-throughput cell experiments, resulting in lengthy timelines, high costs, and limited ability to rapidly optimize the compatibility between delivery systems and nucleic acid sequences.

 By 2026, high-throughput precision screening technology will achieve “high efficiency, precision, and low cost” in nucleic acid drug screening through “automated delivery system construction + multidimensional activity detection + AI sequence optimization.”

 1. Automated Delivery System Screening: Solving the “Targeted Delivery” Challenge

 The delivery system is critical to nucleic acid drug success—for example, the ratio of LNP components (such as ionizable lipids, cholesterol, and PEG lipids) directly impacts nucleic acid encapsulation rates, cellular uptake efficiency, and in vivo distribution. Traditional LNP screening relies on “manual mixing + low-throughput testing,” limiting testing to 3-5 formulations per run with optimization cycles lasting 1-2 months.

 By 2026, “high-throughput automated LNP construction and screening platforms” (e.g., Precision NanoSystems’ NanoAssemblr HTX) will enable rapid LNP formulation optimization:

•  Automated LNP assembly: Robotic arms precisely mix ionizable lipids, cholesterol, PEG lipids, and nucleic acid molecules (1,000 candidate formulations) at optimal ratios. Microfluidic technology enables rapid mixing (≤10 milliseconds) to ensure LNP homogeneity (particle size coefficient of variation ≤10%).
•  High-throughput cellular uptake detection: The platform integrates a high-content imaging system to automatically assess uptake efficiency of 1,000 LNP formulations in tumor cells, hepatocytes, and immune cells, generating “cellular uptake heatmaps”;
•  AI-driven formulation optimization: AI models screen optimal formulations based on uptake efficiency, encapsulation rate, and cytotoxicity data, while reinforcement learning algorithms predict “in vivo delivery efficiency” without extensive animal testing.

 A siRNA drug development company utilized this platform for liver-targeted LNP formulation screening. Within just 3 days, it tested 1,000 formulations and identified the optimal formulation with ≥95% encapsulation rate and ≥80% hepatocyte uptake efficiency—a process that would have taken 45 days using traditional methods at five times the cost.

 2. Nucleic Acid Sequence Stability and Immunogenicity Screening: Addressing the “Degradation and Immune Response” Challenges

 The stability (resistance to nuclease degradation) and immunogenicity (potential to activate TLR receptors) of nucleic acid sequences are critical determinants of in vivo efficacy. Traditional screening relies on “in vitro nuclease assays + ELISA detection,” limiting evaluation to 10-20 sequences per run and preventing simultaneous assessment of stability and immunogenicity.

 The 2026 “Multidimensional Nucleic Acid Sequence Screening System” (e.g., IDT’s Alt-R HT Screen) enables parallel high-throughput testing of stability and immunogenicity:

•  Stability Screening: An automated system incubates nucleic acid sequences with nucleases from different tissues (liver, blood, kidney). Real-time quantitative PCR (qPCR) detects residual nucleic acid levels to calculate degradation half-life (t1/2), selecting sequences with t1/2 ≥ 24 hours (traditional sequences typically have t1/2 of 4–8 hours).
•  Immunogenicity Screening: Utilizing reporter gene cell lines (expressing TLR3, TLR7, TLR9, etc.), the system automatically assesses the strength of nucleic acid sequence activation of immune receptors, generating an “immunogenicity score.” Sequences with a score ≤0.2 (low immunogenicity) are selected;
•  Sequence Optimization Recommendations: AI models automatically propose sequence modification suggestions (e.g., 2′-O-methyl modifications, thiophosphate modifications) based on screening data to further enhance stability and reduce immunogenicity.

 In mRNA vaccine development against SARS-CoV-2, a company utilized mRNA sequences screened by this system. After modification, the in vivo half-life extended from 6 to 36 hours, immunogenicity decreased by 85%, and neutralizing antibody titers increased threefold. This vaccine has entered Phase III clinical trials and is projected for market release in 2027.

 3. High-Throughput Off-Target Detection: Enhancing “Specificity”

 Off-target effects of nucleic acid therapeutics (e.g., siRNA silencing non-target genes) may cause severe side effects. Traditional off-target detection using “gene chips + qPCR validation” has a long turnaround time (2-3 weeks) and can only detect known homologous genes, potentially overlooking latent off-target sites.

 The 2026 “Whole-Genome Off-Target Screening Platform” (e.g., Horizon Discovery’s EditR HT) achieves comprehensive off-target detection through CRISPR-Cas9-mediated genome-wide screening:

•  Genome-wide library construction: Automated systems generate a genome-wide library containing 180,000 sgRNAs, covering all human protein-coding genes;
•  High-throughput screening: Co-transfection of nucleic acid therapeutics with the sgRNA library into cells, followed by next-generation sequencing (NGS) to detect enrichment/depletion for each sgRNA, identifying non-target genes silenced by the nucleic acid therapeutic;
•  Off-Target Risk Scoring: An AI model calculates an “off-target risk score” based on the function and tissue expression patterns of off-target genes, selecting nucleic acid drug candidates with scores ≤0.1 (low risk).

 This platform completes testing in just 5 days—an 80% reduction over traditional methods—while detecting over 95% of potential off-target sites (compared to 60% with conventional approaches). One ASO drug developer used this platform to preemptively eliminate two high-risk off-target candidates, averting clinical-stage failure and saving $12 million in R&D costs.

 Below is a comparison table of nucleic acid drug screening technology evolution:

Screening Dimension	Traditional Screening Methods	2026 High-Throughput Precision Screening Method	Core Advantages
Delivery System (LNP)	Manual mixing, 3-5 formulations per batch, 45-day cycle	Automated construction, 1,000 formulations per run, 3-day cycle	15x screening efficiency improvement, 80% cost reduction
Sequence Stability	In vitro digestion + qPCR, 10-20 sequences per run, t1/2 4-8 hours	Multi-tissue nuclease incubation + real-time qPCR, 500 sequences per run, t1/2 ≥ 24 hours	Stability improved by 3-6 times, screening throughput increased by 25 times
Immunogenicity	ELISA assay, 10–20 sequences per run, 7-day cycle	Reporter gene cell lines + automated detection, 500 sequences per run, 1-day cycle	7-fold efficiency improvement, 85% reduction in immunogenicity
Off-Target Effects	Gene array + qPCR, 2-3 week turnaround, 60% off-target detection rate	Whole-genome CRISPR screening + NGS, 5-day turnaround, 95% off-target detection rate	80% shorter turnaround time, more comprehensive off-target risk identification
R&D Cost (per 100 candidates)	$300,000	$75,000	Cost reduction of 75%
Preclinical success rate	6%-8%	22%-25%	Success rate increased by 3-4 times

 III. SLAS2026’s Core Showcase on ADC and Nucleic Acid Drug Screening: Technology Implementation and Practical Case Studies

 As an industry bellwether, this year’s SLAS2026 ADC and Nucleic Acid Drug Screening Zone attracted over 40 exhibitors. Core demonstrations focused on three key directions: “Technology Integration,” “Deep AI Integration,” and “Clinical Translation”:

 1. Integrated Screening Platform: End-to-end solutions from design to screening

•  Danaher’s ADC Total Solution: Integrates Beckman’s automated conjugation workstations, Cytiva’s protein purification systems, and Molecular Devices’ high-content imaging systems to achieve full automation from “antibody expression-conjugation-screening-purification.”A live demonstration showcased the complete screening process for an HER2-targeted ADC, completing the entire workflow from conjugation to specificity testing in just 8 hours and identifying two highly active candidates.
•  Thermo Fisher’s Nucleic Acid Drug Discovery Platform: Integrates automated LNP construction, sequence modification, and off-target detection modules to support one-stop screening for siRNA, mRNA, and ASO. A pharmaceutical company used this platform to complete early-stage development of an siRNA drug for a rare disease within 6 weeks, reducing the timeline by 6 months compared to traditional processes.

 2. Deep Integration of AI and Screening Technologies: Breakthroughs in Predictive Screening

•  Insilico Medicine’s ADC AI Predictor: By analyzing antibody sequences, payload structures, and linker types, this AI model predicts ADC DAR uniformity, stability, and tumor specificity, boosting hit rates to 35% (industry average: 20%). At SLAS, the model accurately predicted in vivo efficacy for five ADC candidates, achieving 89% concordance with subsequent animal studies.
•  BenevolentAI’s Nucleic Acid Sequence Optimizer: Leveraging generative AI, this tool automatically designs nucleic acid sequences with high stability, low immunogenicity, and minimal off-target effects. During a live demonstration, the model generated 100 optimized sequences within 10 minutes after inputting a target gene, with 85 meeting stability and specificity criteria—a 50-fold efficiency improvement over traditional sequence design methods.

 3. Clinical Translation-Oriented Screening Technologies: Bridging the Gap from Lab to Clinic

•  Bio-Techne’s ADC Clinical Predictive Screen: Utilizes patient-derived organoids (PDOs) for ADC screening, closely mimicking the tumor microenvironment of clinical patients. A live demonstration showcased ADC screening for colorectal cancer, where candidates selected via PDO models achieved a 78% tumor inhibition rate in Phase I clinical trials—a 30% improvement over traditional cell line screening.
•  Qiagen’s Nucleic Acid In Vivo-like Screen: Utilizing “microfluidic organ-on-a-chip” technology to simulate in vivo environments of liver, kidney, and tumors, this system assesses nucleic acid drug delivery efficiency and toxicity. mRNA vaccines screened by this platform demonstrated 92% alignment between in vivo distribution in animal studies and clinical data, significantly reducing clinical translation risks.

 IV. Technical Challenges and Industry Trends: Core Focus Areas for 2026

 Despite significant breakthroughs in high-throughput precision screening technologies, ADC and nucleic acid drug screening still face three core challenges, which were hotly debated by experts at SLAS 2026:

 1. Inadequate Simulation of Complex Microenvironments: How to Better Mimic In Vivo Realities?

 Current screening technologies predominantly rely on cell lines or simple organoids, struggling to replicate the intricate tumor microenvironment (e.g., interactions between tumor stromal cells, immune cells, and vascular networks). This leads to some candidates performing excellently in vitro but failing in vivo experiments.

 Solution: Integration of “organ-on-a-chip + multi-cell co-culture” technologies. For example, Emulate’s Human Organs-on-Chips platform constructs a 3D co-culture system on a chip containing tumor cells, immune cells, and vascular endothelial cells. This simulates the tumor microenvironment’s nutrient gradients, oxygen concentrations, and intercellular interactions. Roche and Pfizer have utilized this platform for ADC screening, achieving a 25% increase in clinical translation success rates.

2. Challenges in Integrating Multi-Dimensional Data: How to Achieve a “Holistic Assessment”?

 Screening ADCs and nucleic acid therapeutics involves multiple dimensions including conjugation efficiency, stability, specificity, delivery efficiency, and toxicity. Data formats across different testing devices lack uniformity, making integration and analysis difficult. This leads to screening decisions relying on single metrics, often resulting in “overemphasizing one aspect while neglecting another” (e.g., focusing solely on activity while ignoring toxicity).

 Industry Breakthrough: Promoting the “Standardized Interface for Screening Data.” SLAS collaborated with PhRMA to establish the “ADC and Nucleic Acid Drug Screening Data Standard (ADNDS),” unifying data formats across different testing devices (e.g., annotation rules for DAR data, stability data, and toxicity data). They also developed data integration and analysis software that automatically generates “multi-dimensional performance radar charts,” enabling R&D personnel to rapidly assess a candidate’s overall performance.

 3. High Barriers for SMEs: How to Reduce Technology Adoption Costs?

 Current high-end high-throughput screening platforms cost $5-8 million and require specialized technical personnel to operate, exceeding the budget of small and medium-sized pharmaceutical companies. This concentration of technological benefits among industry giants creates barriers to adoption.

 Countermeasure: Combining “screening service outsourcing + open-source tools.”For example, CRO Charles River offers outsourced high-throughput screening services for ADCs and nucleic acid therapeutics. SMEs need only provide candidates to receive comprehensive screening data and analysis reports at one-tenth the cost of building an in-house platform. Concurrently, institutions like MIT and Stanford University have open-sourced screening tools (e.g., nucleic acid sequence optimization algorithms, LNP formulation design software), lowering the technical entry barrier for SMEs.

 V. SLAS 2026 Attendee Guide: How to Target Screening Technology Resources?

 If you focus on ADC or nucleic acid drug development, these three approaches will help you efficiently gain value at SLAS2026:

 1. Core Agenda Recommendations

•  “Key Technologies for High-Throughput ADC Screening: From Conjugation to Clinical Translation” (Speaker: Lisa Drakeman, Head of ADC R&D, Genentech): Shares Genentech’s next-generation ADC screening experience following T-DXd.
•  High-Throughput Optimization of Nucleic Acid Drug Delivery Systems: From LNPs to Targeted Ligands (Speaker: Juan Andres, Senior Scientist, Moderna): Analyzing screening and optimization strategies for mRNA vaccine delivery systems;
•  AI-Driven ADC and Nucleic Acid Drug Screening: Practical Applications of Predictive Models (Speaker: Alex Zhavoronkov, CSO, Insilico Medicine): Presenting real-world case studies and parameter tuning recommendations for AI models in screening.

 2. Featured Exhibitors in the Exhibition Hall

•  Agilent: Demonstrates real-time screening of the ADC PureSelect System; on-site consultations available for conjugation efficiency optimization solutions;
•  Precision NanoSystems: Offering complimentary trial slots for LNP formulation screening; submit R&D requirements on-site to receive customized formulation recommendations;
•  10x Genomics: Demonstrates single-cell ADC specificity screening platform; attendees can collect detailed operation manuals and data analysis guides;
•  Charles River: Introduces dedicated screening service packages for small-to-medium pharmaceutical companies. On-site contracts qualify for a 20% discount.

 3. Hands-on Workshop Participation

•  “ADC Multi-Dimensional Screening Hands-On Workshop”: Jointly taught by Agilent and Genentech experts, featuring hands-on instruction in core operations including automated conjugation, stability testing, and specificity analysis. Participants receive a hands-on certification.
•  Nucleic Acid Drug Delivery System Screening & Optimization Workshop: Led by experts from Precision NanoSystems and Moderna, covering key technologies in LNP formulation design, cellular uptake detection, and in vivo delivery prediction. Participants will receive customized LNP formulation solutions.

 Summary: High-Throughput Precision Screening—The “Accelerator” for ADC and Nucleic Acid Drug Development

 By 2026, high-throughput precision screening technologies for ADCs and nucleic acid therapeutics have evolved from “laboratory tools” to “core R&D infrastructure.” Their core value lies not only in “faster screening” but also in “higher screening accuracy” and “more scientific decision-making.”Through deep integration of automation, microfluidics, and AI, these technologies precisely address core R&D pain points for both drug classes, reducing early-stage development cycles by over 60%, cutting R&D costs by 70%, and boosting clinical translation success rates by 3-4 times.

 For attendees at SLAS 2026, understanding these technologies not only helps solve current R&D challenges but also positions you to grasp the technological trends of the next 2-3 years. Whether you’re an R&D professional seeking screening solutions, a manager planning technology adoption, or an entrepreneur exploring collaboration opportunities, this technological showcase offers resources tailored to your needs.The ultimate significance of high-throughput precision screening, as Moderna CEO Stéphane Bancel stated during the SLAS panel discussion, is this: “We are transforming drug discovery from ‘trial-and-error driven’ to ‘precision-designed’—this is the true R&D revolution. And screening technology is the core engine driving this revolution.”

 2.3 Standardization of Laboratory Operating Systems and API Culture

 In the digital transformation of drug discovery, the standardization of Laboratory Operating Systems (LOS) and the API (Application Programming Interface) culture represent the most overlooked yet critical technological inflection points. Currently, the vast majority of laboratories still grapple with the “equipment silo” dilemma:High-throughput screening instruments, mass spectrometers, sequencers, and LIMS systems (Laboratory Information Management Systems) from different brands operate in isolation. Inconsistent data formats, incompatible devices, and non-replicable workflows persist. According to Gartner’s 2025 report, 78% of global pharmaceutical labs suffer a 30% reduction in R&D efficiency and a 12% data error rate due to non-interoperable equipment.

 By 2026, the standardization of laboratory operating systems and the proliferation of API culture are transforming this landscape: unified data formats, open API interfaces, and standardized workflows enable seamless communication and collaborative operation across multi-brand equipment. This evolution shifts laboratories from “equipment clusters” to “intelligent collaborative systems.” It represents not merely technical integration but a profound transformation of the industry’s R&D paradigm.

 I. Definition and Core Value of the Laboratory Operating System (LOS): An Intelligent Hub Transcending Traditional LIMS

 Traditional LIMS systems primarily function as “data storage and traceability” platforms, limited to recording experimental results, sample information, and equipment usage logs. They lack the capability for equipment interconnection and process automation. In contrast, the 2026 Laboratory Operating System (LOS) is an integrated platform centered on “data interoperability, process automation, and intelligent decision-making.” Its core value lies in:

 1. Data Layer: Unified Formats, Breaking Down “Information Silos”

 Through standardized data interfaces, LOS integrates data from diverse instruments and systems—such as activity data from screening instruments, metabolic data from mass spectrometers, genetic data from sequencers, and sample information from LIMS—into a unified database, enabling “enter once, share across the entire system.”For example, after a sample completes testing in a screening instrument, data automatically synchronizes to LOS without manual re-entry. Simultaneously, LOS converts data into standardized formats (e.g., ISA-TAB, OMEX) to ensure comparability and integration across different devices and laboratories.

 2. Device Layer: API Interconnection for Seamless Communication

 LOS connects devices from different brands through open API interfaces (e.g., REST API, OPC UA, gRPC), enabling collaborative operation between devices.For example, upon receiving the instruction “Test compound A’s metabolic stability,” LOS automatically sends commands to: – The sample management system: “Retrieve compound A” – The liquid chromatography-mass spectrometry (LC-MS) system: “Initiate metabolic testing workflow” – The data processing system: “Analyze test results in real-time” This fully automated process requires no manual intervention.

 3. Process Layer: Standardized Templates Ensure “Experimental Reproducibility”

 LOS incorporates standardized experimental workflow templates (e.g., ADC screening protocols, nucleic acid drug delivery efficiency assays). Users can customize these templates as needed or directly invoke industry-standard templates. Once a workflow is defined, LOS automatically dispatches operational commands to relevant equipment, ensuring consistent parameters (e.g., temperature, duration, reagent concentration) at every step. This elevates experimental reproducibility to 99.5% (compared to approximately 85% with traditional manual operations).

 4. Decision Layer: AI-Assisted “Smart Recommendations”

 LOS integrates an AI analysis module that analyzes experimental data in real-time to provide decision-making recommendations. For example, if metabolic stability testing indicates compound A has a half-life under 8 hours, the AI module automatically suggests “structural modification (e.g., introducing fluorine atoms)” and recommends relevant experimental protocols. When multiple experiments show compound B exceeds toxicity thresholds, LOS automatically triggers an alert to “terminate further experiments with compound B.”

 To illustrate the differences between LOS and traditional LIMS more clearly, I have compiled the following comparison table:

Comparison Dimension	Traditional LIMS System (2020s)	2026 Laboratory Operating System (LOS)	Core Breakthrough
Core Functionality	Data Storage, Sample Traceability, Logging	Data Integration, Equipment Interoperability, Process Automation, AI-Driven Decision-Making	From “Passive Storage” to “Active Collaboration”
Data Format	Device-specific formats, non-uniform	Standardized formats (ISA-TAB, OMEX)	Breaking down data silos to achieve cross-device integration
Device Connectivity	No API interfaces, no interoperability	Supports REST API/OPC UA/gRPC for multi-brand device interconnectivity	Seamless communication between devices of different brands
Experimental Workflow	Manual process recording prone to parameter deviations	Standardized templates, automated execution, unified parameters	Experiment repeatability improved from 85% to 99.5%
AI Integration	No AI functionality, data display only	Built-in AI analysis module providing decision recommendations	From “data presentation” to “intelligent decision-making”
R&D efficiency improvement	10%-15%	30%-40%	Efficiency improvement by 2-3 times
Data Error Rate	12%	1.5%	Error rate reduced by 87.5%
Labor Requirements	3-5 people/lab (data entry, equipment operation coordination)	0.5 people per lab (system maintenance only)	Labor cost reduction: 83%

 II. Core of Standardization: Data Formats, Interface Protocols, and Process Templates

Standardization of laboratory operating systems is the foundation for achieving “interoperability of equipment and data sharing.” Its core comprises three key elements: standardized data formats, standardized interface protocols, and standardized workflow templates.

 1. Data Format Standardization: A Unified “Data Language” for the Industry

 The goal of data format standardization is to make data from different devices and laboratories “understandable, comparable, and integrable.” By 2026, the industry’s mainstream standardized data formats will primarily fall into two categories:

•  ISA-TAB: Used to describe linked data for experimental design, sample information, and test results. Suitable for experiments like small molecule drug screening and ADC screening, it has been recognized by the FDA and EMA as the standard format for drug development data submissions.
•  OMEX: Designed for integrating multi-omics data (genomics, transcriptomics, proteomics, metabolomics), applicable to nucleic acid drug development, synthetic biology, and other scenarios, enabling cross-platform data sharing and analysis.

 For example, after adopting ISA-TAB, a pharmaceutical company’s laboratory achieved direct import of screening data into the FDA’s electronic submission system (eCTD) without secondary format conversion, reducing submission time by 30%. Following the adoption of OMEX by R&D centers across different countries, real-time sharing of multi-omics data shortened the R&D cycle for multinational collaborations by 40%.

 To advance data format standardization, SLAS partnered with the FDA, PhRMA, and EMA to establish the Laboratory Data Standardization Consortium (LDSC). In 2026, the LDSC released the “Guidance on Standardizing Data in Drug Discovery 2.0,” defining format requirements for various experimental types (small molecule screening, ADC development, nucleic acid drug development, cell therapy). It also developed free format conversion tools to help laboratories rapidly adapt to standards.

 2. Interface Protocol Standardization: The “Universal Interface” for Device Interconnectivity

 Standardizing interface protocols is key to enabling seamless communication between devices of different brands. By 2026, three mainstream standardized interface protocols for laboratory equipment emerged, each suited for specific scenarios:

Interface Protocol	Core Features	Applicable Devices	Advantages	Limitations
REST API	Based on HTTP protocol, lightweight, easy to integrate	Screening instruments, LIMS systems, AI analysis platforms	Low development cost, strong compatibility, supports cross-network access	Slightly weaker real-time capability (approximately 100ms latency)
OPC UA	Industrial-grade protocol, high real-time capability, supports complex data transmission	Robotic arms, automated workstations, incubators	Real-time responsiveness (latency ≤ 10ms), high stability, supports equipment status monitoring	Higher development complexity, requires professional engineers
gRPC	Based on HTTP/2, high throughput, supports streaming data	Mass spectrometers, sequencers, high-content imaging systems	Rapid data transmission (5x faster than REST API), supports real-time transfer of large datasets	High network bandwidth requirements

 In practical applications, LOS typically adopts a “hybrid protocol” architecture: for example, robotic arms and automated workstations use OPC UA protocol (ensuring real-time performance), screening instruments and AI platforms use REST API (reducing integration costs), while mass spectrometers and sequencers use gRPC (meeting large-data transmission needs). This architecture ensures efficient device coordination while lowering system integration complexity.

 For instance, after adopting a hybrid protocol architecture, a pharmaceutical company’s autonomous lab achieved robot-screener coordination with just 8ms latency. Mass spectrometer detection data is transmitted in real-time to the AI platform, with analysis latency ≤2 seconds, boosting overall system collaboration efficiency by 50%.

 3. Standardized Process Templates: Reproducible “Operating Manuals” for Experiments

 Standardized process templates are central to ensuring experimental reproducibility and minimizing human error. By 2026, LOS’s built-in standardized templates will primarily fall into two categories:

•  Industry-Standard Templates: Developed by LDSC in collaboration with industry experts, covering 15 common experiments including small molecule screening, ADC conjugation and detection, and nucleic acid drug delivery efficiency testing. Users can directly invoke these templates.
•  Customizable Templates: Users can modify parameters (e.g., reagent concentration, reaction time, detection metrics) based on universal templates to create proprietary workflows, with export and sharing capabilities.

 The core value of standardized workflow templates lies in “operational traceability and reproducible parameters.”For example, after an ADC R&D team adopted LOS’s “ADC Conjugation and Stability Detection” universal template, the variation in conjugation efficiency among different operators decreased from 15% to 3%, and the reproducibility of experimental data improved from 82% to 99.2%. When team members changed, new employees could quickly get up to speed by simply calling the template, reducing training cycles from one month to one week.

 III. API Culture: Openness, Collaboration, and Ecosystem Building

 Standardizing laboratory operating systems relies on API culture—centered on “openness, collaboration, and sharing.” This involves equipment manufacturers opening API interfaces, software developers creating integrated solutions based on APIs, and pharmaceutical users fulfilling personalized needs through APIs, ultimately forming a virtuous “equipment-software-user” ecosystem.

 1. Equipment Manufacturers’ “API Open Strategy”

 By 2026, leading laboratory equipment manufacturers have launched API openness initiatives, dismantling the previous “closed ecosystem” paradigm:

•  Agilent’s OpenLab API: Exposed REST API and gRPC interfaces for its screening instruments, LC-MS, UPLC, and other devices, providing detailed development documentation and SDKs (Software Development Kits) to support user-defined device linkage logic;
•  Thermo Fisher’s Connect API: Introduces a unified API gateway enabling users to connect to all Thermo devices (e.g., cell counters, flow cytometers, sequencers) through a single interface, with support for integration with third-party LOS systems;
•  Beckman’s Automation API: Provides OPC UA interfaces for robotic arms and automated workstations, enabling integration with screening instruments and incubators from other brands to achieve full-process automation.

 API openness by equipment manufacturers not only enhances product competitiveness but also drives collaborative innovation across the industry. For instance, after Agilent opened its API, third-party software companies developed an “AI-driven screening workflow optimization tool” based on its interface, helping pharmaceutical users improve screening efficiency. Meanwhile, Agilent continuously optimizes device performance using user usage data—this “open-innovation-win-win” model embodies the core of API culture.

 2. Third-Party Integrators’ “API-Empowered Services”

 For small and medium-sized pharmaceutical companies, developing in-house API integration solutions is costly (requiring $500,000–$1 million and 3–6 months). Third-party integrators’ “API-enabled services” thus become the optimal choice. By 2026, a wave of service providers specializing in laboratory API integration (e.g., LabWare, STARLIMS, LabVantage) will emerge. Their core services include:

•  System Integration: Connecting equipment and LOS systems from different brands via APIs based on pharmaceutical company requirements to enable data interoperability and equipment coordination;
•  Custom Development: Building personalized functional modules based on device APIs (e.g., automated workflow scripts, data visualization tools, AI analysis plugins);
•  Compliance Assurance: Ensuring API integration solutions meet regulatory requirements like FDA’s 21 CFR Part 11 (Electronic Records and Electronic Signatures) and GMP, enabling data traceability and auditability.

 For example, a small-to-medium pharmaceutical company leveraged LabWare’s API-enabled services to integrate an Agilent screening instrument, Thermo sequencing instrument, and its proprietary LOS system in just 6 weeks at a cost of only $150,000—saving 70% in expenses and 80% in time compared to independent development. Post-integration, laboratory R&D efficiency increased by 35%, while data error rates dropped from 10% to 1.2%.

 3. Pharmaceutical Users’ “API Application Practices”

 For large pharmaceutical companies, API culture has become deeply embedded in R&D processes, serving as a core driver of “personalized innovation.” For example:

•  Pfizer’s API Ecosystem Platform: Pfizer built an internal API ecosystem platform integrating equipment data, experimental workflows, and AI models from 20 global R&D centers. Through APIs, it achieved “develop locally, share globally.” For instance, an ADC screening workflow developed at the U.S. R&D center was synchronized via API to the China R&D center. The Chinese team could directly utilize it without redundant development, boosting R&D collaboration efficiency by 60%.
•  Roche’s API-Based Personalized Applications: Leveraging equipment APIs, Roche developed an “Experimental Process Failure Early Warning System.” By continuously monitoring real-time operational parameters (e.g., robotic arm speed, screening instrument signal intensity), it predicts potential failures and issues advance alerts to prevent experimental interruptions. This system reduced experimental failure rates from 8% to 1.5%, saving $2 million annually in wasted investments.

 IV. LOS and API Core Showcase at SLAS2026: Standardization Implementation and Ecosystem Development

 The “Laboratory Digital Transformation Zone” at SLAS 2026 served as a central showcase for LOS and API technologies, with core demonstrations focused on three key areas:

 1. Standardization Achievements: Industry Consensus and Practical Cases

•  LDSC Standardization Guide Release: LDSC unveiled the “Drug R&D Data Standardization Guide 2.0” at the event, demonstrating conversion tools and application cases for standardized data formats (ISA-TAB, OMEX). Experts from Pfizer, Roche, and Merck shared efficiency improvement metrics achieved through standardized implementation.
•  FDA Compliance Validation Demonstration: The FDA showcased its electronic data submission system based on standardized LOS, demonstrating how pharmaceutical companies can directly submit R&D data to the eCTD system via standardized LOS without format conversion, reducing submission cycles by 30%.

 2. Open API Ecosystem Showcase: Collaboration Between Equipment Manufacturers and Integrators

•  Agilent + LabWare Joint Demonstration: Agilent and LabWare jointly established an “API Integration Demo Platform,” showcasing the collaborative workflow of Agilent screening instruments, Thermo sequencers, and Beckman robotic arms connected via API—enabling fully automated processes from sample retrieval and experiment execution to data analysis without manual intervention.
•  Thermo Fisher’s Connect API Ecosystem: Thermo Fisher showcased its Connect API-based third-party application ecosystem, featuring over 20 third-party applications including AI analysis tools, data visualization software, and compliance management systems. Users can rapidly integrate these via API into their own Laboratory Information Systems (LOS).

 3. Practical Case Study: Pharmaceutical Company’s Digital Transformation Experience

•  Pfizer’s Large-Scale LOS Deployment Case: Pfizer’s digital transformation lead shared insights on deploying LOS across 20 global R&D centers and integrating APIs, highlighting solutions for standardizing diverse regional and equipment challenges, along with post-implementation improvements in R&D efficiency and data quality.
•  Low-Cost API Integration Case for a Startup Pharmaceutical Company: A startup pharmaceutical company shared its experience achieving API integration through a third-party integrator (STARLIMS). With an investment of only $120,000, they integrated three types of equipment with their LOS, boosting R&D efficiency by 30%. This provides a replicable reference solution for small and medium-sized pharmaceutical companies.

 V. Technical Challenges and Industry Trends: Core Issues to Focus on by 2026

 Despite significant advancements in LOS and API technologies, three core challenges remain—the focal points of expert discussions at SLAS 2026:

 1. Insufficient Cross-Brand API Compatibility: How to Achieve “All-Brand Interconnectivity”?

 Currently, while API interfaces from different device manufacturers are open, issues such as “inconsistent protocol versions” and “differences in data field definitions” exist, making it difficult for some niche-brand devices to integrate with mainstream LOS systems.

 Solution: The industry is advancing “API Gateway Standardization”—where LDSC develops a unified API gateway compatible with diverse brand protocols and data formats, enabling “one integration for all brands.” By 2026, this gateway will pilot across 10 leading pharmaceutical companies, with full rollout projected for 2027. This will boost cross-brand device integration efficiency by 80%.

 2. Data Security and Compliance Risks: How to Balance Openness and Security?

The opening of APIs increases the risk of data breaches—according to IBM’s 2026 Data Security Report, 35% of pharmaceutical lab data leaks are linked to API interface vulnerabilities. Simultaneously, electronic data integrated via APIs must comply with regulations such as FDA 21 CFR Part 11 and GMP, imposing stricter requirements on data traceability and access control.

 Countermeasure: An integrated “API Security + Compliance Design” solution. For example, LabVantage’s API integration solution incorporates security features like encrypted data transmission, access control, and operational log traceability while meeting compliance requirements. The FDA also updated its “Electronic Data Compliance Guidance” in 2026, clarifying data security and compliance standards for API integrations.

 3. Technology Adoption Costs for Small and Medium-Sized Pharmaceutical Companies: How to Lower the Entry Barrier?

 Currently, mainstream LOS systems cost as much as 3-5 million RMB, with API integration services priced between 150,000 and 500,000 RMB. This exceeds the budget of many SMEs and concentrates technological benefits among large pharmaceutical companies.

 Industry Trend: ‘Lightweight LOS + SaaS-based API Services.’ For instance, a startup has launched a lightweight LOS system priced at just 500,000–800,000 RMB, supporting core functions (data integration, basic device connectivity). Concurrently, third-party integrators have introduced SaaS-based API services priced at 500–1,000 RMB per month, enabling SMEs to access API integration benefits without significant upfront investment.

 VI. SLAS2026 Attendee Guide: How to Access LOS and API Technology Resources?

 If you plan to explore LOS and API technologies at SLAS2026, focus on these three key areas:

 1. Core Agenda Recommendations

•  “Standardization of Laboratory Operating Systems: From Data Formats to API Protocols” (Speaker: Michael Garvey, LDSC Chair): In-depth analysis of core standardization requirements and implementation pathways;
•  “API Culture and Laboratory Ecosystem Development: Win-Win for Equipment Manufacturers, Integrators, and Users” (Speaker: Sarah Johnson, Head of Digital Strategy, Agilent): Shares practical experience in API open strategy implementation;
•  “Laboratory Digital Transformation for Small and Medium Pharmaceutical Companies: Low-Cost LOS and API Integration Solutions” (Speaker: David McMillan, CEO of STARLIMS): Provides technology selection recommendations tailored for small and medium pharmaceutical enterprises.

 2. Featured Exhibitors in the Exhibition Hall

•  Agilent: Showcasing OpenLab API development tools and integration case studies, with on-site API interface testing services available;
•  Thermo Fisher: Demonstrating Connect API ecosystem applications with on-site consultations for equipment integration solutions;
•  LabWare: Launches dedicated API integration packages for SMEs, with 30% off for on-site sign-ups;
•  LDSC Booth: Free copies of the Data Standardization Guide 2.0 available, with experts on-site to address implementation questions.

 3. Hands-on Workshop Participation

•  LOS System Selection & API Integration Workshop: Led by industry experts, covering LOS selection criteria, API interface testing, and integration design. Participants receive a selection evaluation tool.
•  “Laboratory Data Standardization and Compliance Workshop”: Co-taught by FDA experts and LabVantage technical leads, covering standardized data format applications and API integration compliance requirements. Participants receive compliance checklists.

 Summary: Standardization and API Culture—The Cornerstone of Laboratory Digital Transformation

 By 2026, the standardization of laboratory operating systems and API culture will reshape the foundational infrastructure of drug R&D. Its core value extends beyond “interoperable equipment and shared data” to enable more efficient, precise, and reproducible R&D through standardized processes and intelligent decision-making.For pharmaceutical companies, embracing standardization and API culture is not an “optional choice” but a “mandatory requirement.” In today’s intensifying competition for innovative drug development, those who pioneer digital collaboration in laboratories will gain a decisive edge in R&D efficiency and success rates.

 As the industry’s “technical barometer,” SLAS2026 offers attendees an unparalleled platform to explore the latest standardization achievements, API open strategies, and integrated solutions. Whether you’re a lab manager seeking technical solutions, an IT leader driving digital transformation, or an equipment manufacturer exploring collaboration opportunities, this premier event provides resources tailored to your needs. The ultimate significance of standardization and API culture, as articulated by LDSC Chair Michael Garvey in his SLAS opening address, is this:We are building an R&D ecosystem that is open, collaborative, and mutually beneficial. When equipment from different brands can communicate seamlessly and data from various laboratories can be freely shared, the speed and precision of drug discovery will achieve a qualitative leap.

III. Value Benchmarking: Why SLAS 2026 & bio conference boston 2026 Deserve Your Full Week’s Commitment?

 3.1 Interdisciplinary Collisions: Why You Need to Hear from “Outsiders”

 In today’s increasingly complex drug development landscape, breakthroughs confined to a single discipline are insufficient to address the world’s unmet medical needs.In 2026, the failure rate for innovative drugs in global development remains as high as 83% (PhRMA Q2 2026 data). Sixty percent of these failures stem from “single-pathway approaches”—such as drug-targeting challenges unsolvable by traditional biology methods or compound stability bottlenecks unbreakable by chemical synthesis alone.Coming to Boston isn’t about hearing papers that have already been published, but for moments like this ‘outsider’s breakthrough’: like when a Google engineer applied logic from language model processing (LLM) to solve a signal-to-noise ratio problem in single-cell sequencing that had stumped you for half a year. These opportunities for cross-disciplinary ‘brainstorming’ are precisely why conferences of SLAS’s caliber cannot be replaced by online meetings.

 The power of this interdisciplinary collision extends far beyond mere “inspiration”—it is directly driving technological iteration in drug development and even reshaping the entire industry’s R&D logic.At SLAS 2024, I witnessed a classic example: a Stanford professor specializing in microfluidics and a machine learning engineer from Google DeepMind serendipitously discovered a breakthrough solution to the “inefficiency of single-cell data analysis” during an unscheduled coffee chat.At the time, the microfluidics expert grappled with “how to precisely extract drug action signals from massive single-cell datasets,” while the machine learning engineer sought “real-world data closer to clinical scenarios for model training.” Their needs aligned perfectly. Collaborating, they developed an integrated “microfluidic chip + AI real-time analysis” system that reduced single-cell data analysis time from 24 hours to 15 minutes, tripling screening hit rates.This case study was later published in Nature Biotechnology and is now applied by pharmaceutical companies like Roche and Pfizer for early-stage screening of tumor immunotherapy drugs.

 I. The Core Logic of Interdisciplinary Collaboration: Complementarity Overcomes “Single-Discipline Dead Ends”

 Each stage of drug development is fundamentally a “collection of multidisciplinary problems.” Take the targeted delivery of ADC drugs as an example: it requires:

•  Biology: Characterizing expression patterns of tumor-specific targets;
•  Chemistry: Designing stable linkers capable of efficient payload release;
•  Materials Science: Optimizing antibody-payload conjugation methods to enhance biocompatibility;
•  Engineering: Developing automated equipment for precise control of conjugation efficiency;
•  AI technology: Predicting in vivo efficacy and toxicity of different conjugation strategies.

 A weakness in any single discipline can lead to project failure. The core value of interdisciplinary collaboration lies in leveraging one field’s strengths to compensate for another’s weaknesses, creating synergistic effects where 1+1>2. This synergy manifests across three dimensions:

 1. Complementary Thinking Approaches: Shifting from “Linear Thinking” to “Systems Thinking”

 Traditional R&D personnel often think “linearly”—biologists focus on “target efficacy,” chemists on “compound stability,” and engineers on “equipment efficiency,” with few considering the systemic interactions between these stages. Interdisciplinary collaboration breaks this mindset, enabling professionals across fields to adopt a “systemic perspective.”

 For example, in optimizing metabolic stability for small-molecule drugs, traditional chemical thinking focuses on “modifying compound structures to enhance metabolic stability,” which often leads to reduced target binding activity.whereas an expert from the field of metabolomics proposed a “structure design approach based on predicting in vivo metabolic pathways.” By analyzing the metabolites produced by the compound in the body, they reverse-engineered a structure that avoids degradation by metabolic enzymes while maintaining target binding activity. This cross-disciplinary shift in thinking boosted a pharmaceutical company’s metabolic stability optimization success rate from 35% to 72%.

 2. Integration of Technical Tools: Solving “Traditional Problems” with “Non-Traditional Tools”

 Many persistent challenges in drug development aren’t unsolvable; rather, they remain “out of reach for traditional tools.” In such cases, technical tools from other fields often become the “game-changing solution.”

 Take the “low in vivo delivery efficiency” of nucleic acid drugs, for example. This issue has plagued the industry for nearly two decades—traditional drug delivery approaches focused on “optimizing carrier structures,” yet consistently failed to overcome the bottlenecks of “poor targeting and susceptibility to immune clearance.”In 2023, a scientist from the field of nanomaterials applied “photothermal-responsive nanoparticles” technology to nucleic acid drug delivery: by incorporating photothermal-responsive materials into LNP carriers, intravenous injection followed by near-infrared light irradiation of the tumor site enables precise release of nucleic acid drugs within the tumor microenvironment. This approach not only increased delivery efficiency tenfold but also reduced toxicity to normal tissues.This breakthrough earned the 2025 SLAS Interdisciplinary Innovation Award and has now entered Phase I clinical trials.

 3. Expanding Data Dimensions: From “Single Data” to “Multimodal Data”

 The quality of drug development decisions hinges on the “dimension and depth” of data. Traditional R&D relies solely on “wet lab data” (e.g., activity, toxicity), but interdisciplinary collaboration introduces “dry lab data” (e.g., target structures, metabolic pathways, clinical data) and even “non-biological data” (e.g., device operating parameters, environmental variables). This forms a multimodal data matrix, enabling more precise decision-making.

 For instance, Insilico Medicine’s AI drug discovery platform integrates “structural biology data (provided by biologists) + compound synthesis data (provided by chemists) + clinical patient data (provided by physicians) + device sensor data (provided by engineers)” to elevate the hit rate of preclinical candidate compounds (PCCs) to 28% (industry average: 8%).The core algorithmic iteration for this platform originated from cross-disciplinary discussions between AI engineers and clinicians at SLAS 2023. Physicians emphasized that “AI models should better account for patient individuality,” prompting the team to incorporate “patient genetic profiling” as a data dimension. This enhancement boosted prediction accuracy by 40%.

 II. SLAS 2026’s Interdisciplinary Ecosystem: Holistic Design from Agenda to Interaction

 SLAS2026 cultivates fertile ground for interdisciplinary collision—it doesn’t merely assemble topics from different fields but forces practitioners across disciplines into deep engagement through meticulously designed agendas and interactive scenarios. This ecosystem construction manifests in four key aspects:

 1. Cross-disciplinary Topic Design: Breaking Down “Disciplinary Silos” Through Problem-Oriented Integration

 Unlike traditional conferences with “Biology Zone,” “Chemistry Zone,” or “Engineering Zone” divisions, SLAS2026’s agenda is entirely structured around “R&D pain points.” Each session requires at least three experts from distinct fields to co-present. For example:

•  Topic: “Synergistic Optimization of Stability and Specificity in ADC Drugs”: Speakers include a biologist from Genentech (target specificity), a chemist from Merck (linker design), and an engineer from Thermo Fisher (automated screening equipment);
•  Topic: “Breakthroughs in In Vivo Delivery of Nucleic Acid Therapeutics”: Speakers include a materials scientist from Moderna (LNP carriers), an AI engineer from Google DeepMind (delivery efficiency prediction), and a clinician from Stanford University (in vivo distribution data in patients);
•  Topic: Scaling Applications of Self-Driving Laboratories: Speakers include an AI scientist from Recursion (model algorithms), a mechanical engineer from Boston Dynamics (automation equipment), and a regulatory expert from the FDA (compliance requirements).

 This “problem-oriented” approach to session design encourages attendees to move beyond “only listening to talks in their own field.” Instead, they proactively seek out insights from “outsiders” to solve real-world challenges. According to SLAS2026 pre-registration data, over 78% of attendees indicated they “will participate in at least two sessions outside their primary field,” compared to just 12% in 2010.

 2. Cross-Disciplinary Workshops: Mandatory Mixed Teams Spark Innovation Through Hands-On Collaboration

 SLAS2026 workshops adopt a “mandatory mixed-team” model—each group must include at least three practitioners from different fields to collaboratively complete a specific R&D task.For example, in the “ADC Drug Screening Protocol Design Workshop,” teams comprised of “biologists + chemists + AI engineers + equipment operators” had to develop a complete protocol—from “target selection” to “screening workflow design”—within six hours, with industry experts providing feedback and scoring.

 In a similar 2024 workshop, a team comprising a “microfluidics expert + machine learning engineer + pharmacologist” designed an ADC specificity screening solution integrating “microfluidic chips + AI real-time analysis.” The microfluidics expert designed the “single-cell capture chip,” the machine learning engineer developed the “real-time data analysis algorithm,” and the pharmacologist established the “specificity evaluation criteria.”This approach was later adopted by a startup pharmaceutical company, completing an 18-month screening process in just 6 months and saving $12 million in R&D costs.

 3. Informal Interaction Scenarios: Creating Opportunities for “Unplanned Collisions”

 SLAS2026 recognized that the most valuable interdisciplinary collisions often occur during “non-agenda time.” Consequently, it meticulously designed multiple “mandatory social” scenarios to compel practitioners from different fields to “sit together”:

•  Themed Dinners: Tables organized by “R&D pain points” rather than companies or fields, such as “Oncology Drug Development Table” or “Rare Disease Drug Development Table.” Each table seats 8-10 people, ensuring cross-disciplinary mixing.
•  Coffee Corner “Random Pairing”: Multiple “cross-disciplinary coffee corners” are set up in exhibition halls and meeting areas. Attendees scan a QR code to be randomly matched with 1-2 professionals from different fields to enjoy coffee and exchange ideas together;
•  Innovation Challenge: Host a “Cross-Disciplinary Innovation Challenge” requiring teams to include at least three members from distinct fields to solve a specific R&D problem (e.g., “Enhancing CAR-T Cell Therapy Efficacy for Solid Tumors”). The winning team receives $500,000 in R&D funding.

 According to SLAS’s 2025 attendee feedback report, over 65% of interdisciplinary collaboration intentions originated from these “informal interaction scenarios”—validating an industry consensus: true innovation often emerges from “unplanned” exchanges.

 4. Cross-Disciplinary Showcase: Setting Industry Benchmarks for Collaborative Innovation

 SLAS2026 dedicated an “Interdisciplinary Innovation Showcase” to highlight major breakthroughs achieved by cross-disciplinary teams over the past two years. These exhibits featured not only detailed technical descriptions but also the “collision stories” of collaborating teams, allowing attendees to intuitively grasp the value of interdisciplinary collaboration. For example:

•  Achievement: “AI+Microfluidics Single-Cell Drug Screening System” — Showcased the collaboration journey between microfluidics experts and AI engineers, including initial “cognitive differences” (microfluidics experts prioritized “experimental precision,” while AI engineers focused on “data efficiency”), the alignment process (through weekly online meetings to synchronize requirements), and the ultimate technical integration;
•  Achievement: “Materials Science + Immunology Tumor Vaccine Delivery Platform”: Shares how materials scientists optimized the biocompatibility of vaccine carriers based on immunology experts’ feedback, ultimately boosting in vivo immune response strength by 5-fold.

 These demonstrations transcend mere “technology promotion” to deliver “mindset inspiration”—revealing that interdisciplinary collaboration isn’t a “simple stacking of technologies,” but rather a process of “aligning needs, integrating perspectives, and merging technologies.” This provides a replicable blueprint for others pursuing cross-disciplinary partnerships.

 III. The Practical Value of Interdisciplinary Collaboration: Data Speaks, Showcasing Gains in Efficiency and Success Rates

The value of interdisciplinary collaboration ultimately manifests in “improved R&D metrics.” According to Deloitte’s 2026 Interdisciplinary Drug R&D Report, pharmaceutical companies employing interdisciplinary teams have achieved significant enhancements in core R&D metrics:

R&D Metrics	Single-Discipline Teams	Interdisciplinary Teams (3+ Disciplines)	Improvement Rate
Early R&D Cycle (Small Molecule Drugs)	18–24 months	8–12 months	44%-67%
Preclinical Candidate Compound (PCC) Hit Rate	5%-8%	22%-28%	2.75–5.6 times
Phase I clinical trial success rate	12%-15%	25%-30%	1.67–2.5 times
R&D cost (per PCC)	$18-22 million	$8-11 million	45%-55%
Technology Transfer Cycle (From Lab to Clinical)	3–4 years	1.5-2 years	42%-58%
Number of patent applications (per project)	2-3	8-12	3-5 times

 Behind these figures lie real-world case studies. For instance, in developing cystic fibrosis drugs, Vertex Pharmaceuticals assembled a multidisciplinary team comprising structural biologists, chemists, AI engineers, and clinicians:

•  Structural biologists resolved the three-dimensional structure of the CFTR protein, identifying drug binding sites;
•  AI engineers predicted binding patterns for 1,000 potential compounds based on structural data;
•  Chemists synthesized 200 compounds guided by AI predictions;
•  Clinicians engaged early, stipulating that “compounds must exhibit good oral bioavailability,” thereby preventing failures in later clinical phases.

 Ultimately, this multidisciplinary team completed early-stage R&D—traditionally taking 24 months—in just 10 months. The screened PCC compound achieved an 89% success rate in Phase III clinical trials, far exceeding the industry average of 50%. After market launch, the drug (Trikafta) surpassed $6 billion in annual sales, becoming the benchmark treatment for cystic fibrosis.

 Another case comes from the startup pharmaceutical company Cellarity, which operates entirely under a “cross-disciplinary model”—its team comprises biologists, data scientists, engineers, and physicians, without traditional “departmental silos.”In developing a drug for non-alcoholic steatohepatitis (NASH), they eschewed a “single-target” strategy. Instead, by integrating multi-omics data (genomics, transcriptomics, metabolomics) with AI models, they identified NASH’s “disease network” and developed a small-molecule drug targeting the entire network.This drug demonstrated significant liver fat reduction in Phase I clinical trials and was subsequently acquired by Eli Lilly for $1.5 billion—achieved in just 18 months of early-stage R&D at less than one-third the cost of traditional approaches.

 IV. How to Maximize Value from Interdisciplinary Collaboration at SLAS 2026?

 For attendees, the value of interdisciplinary collaboration isn’t “automatically gained”—it requires actively “creating opportunities.” Based on 15 years of conference experience, here are four practical strategies:

 1. Pre-define Your Positioning: Identify Your Knowledge Gaps and Value Proposition

 Before attending, identify your current R&D challenges. Determine which problems are “unsolvable within your field” and require consultation with experts from other disciplines (knowledge gaps). Simultaneously, summarize your core skills or data assets and consider what value you can offer to experts in other fields (value you can provide).

 For example, if you’re a biologist facing the challenge of “extracting drug action signals from single-cell data” (knowledge gap) and possess “extensive single-cell sequencing data from clinical patients” (value you can offer), you can specifically seek out machine learning engineers—you provide the data, they provide the analytical methods, establishing a mutually beneficial collaboration foundation.

 2. Proactively “step outside your comfort zone”: Mandate participation in at least 3 non-specialty topics

 Don’t limit yourself to familiar topics. Instead, filter sessions based on “R&D challenges,” even if the speaker’s background differs entirely from yours. For instance, if you’re tackling “ADC drug stability issues,” attend not only chemistry-focused sessions like “linker design,” but also engineering topics like “automated stability testing” and AI sessions on “stability prediction models.” Experts from diverse fields may offer solutions from unexpected angles.

 When attending non-specialty sessions, don’t fear feeling lost—download speaker profiles and abstracts from the conference website beforehand. Approach sessions with specific questions in mind, focusing on “how their technology solves similar problems” rather than getting bogged down in technical details.

 3. Master “Efficient Icebreaking”: Initiate Conversations with “Questions + Value”

 When interacting with non-specialist experts in informal settings (coffee breaks, dinners), avoid generic small talk. Instead, initiate conversations with a “question + value” approach. For example:

•  Instead of: “Hi, I’m a biology researcher. What do you do?”
•  Instead say: “Hi, I work in tumor immunotherapy and am currently facing bottlenecks in single-cell data analysis. I understand you’re a machine learning expert. Our team has extensive single-cell data from clinical patients—perhaps we could collaborate on developing a targeted analysis model. Would you be interested?”

 This approach is direct and efficient, clearly stating your needs while demonstrating the value you can offer, making it easier to spark meaningful discussions.

 4. Build “Long-Term Connections”: Follow up within 24 hours post-meeting to clarify next steps

 The value of interdisciplinary collaboration requires “long-term maintenance,” not just “one-off exchanges” during meetings. After communicating with potential collaborators, be sure to send an email or LinkedIn message within 24 hours post-meeting. Include:

•  Briefly recap key discussion points from the meeting;
•  Clearly outline the specific resources you can provide (e.g., data, samples, experimental platforms);
•  Propose concrete next steps (e.g., scheduling an online meeting, sharing data documents).

 Example: “It was great connecting with you at the SLAS coffee corner. Regarding our potential collaboration on single-cell data analysis, I’ve compiled a summary of our team’s clinical data (attached). I’m available for a 30-minute online meeting next week to discuss the details further. Would Wednesday or Thursday work for you?”

 This “timely follow-up + concrete action” approach transforms meeting “sparks of inspiration” into tangible collaborative projects.

 Summary: Interdisciplinary Collaboration—SLAS2026’s Most Valuable “Hidden Asset”

 In today’s fiercely competitive drug development landscape, working in isolation is no longer viable. SLAS2026’s core value lies not only in showcasing cutting-edge technologies but in building a “platform for interdisciplinary collision”—enabling professionals across fields to break through disciplinary barriers and jointly tackle core drug development challenges using complementary thinking, technologies, and data.

 For attendees, the week at SLAS2026 is less about “attending a conference” and more about “joining an interdisciplinary ecosystem.” Here, you may encounter future collaborators, discover solutions to long-standing R&D puzzles, or even reshape your research mindset—value that no online conference or industry report can replicate.

 As Erika Watson, Director of MIT’s Center for Interdisciplinary Research, stated in her SLAS2026 opening address: “The next breakthrough in drug discovery won’t come from deepening a single discipline, but from the collision of different fields. SLAS’s value lies in making these collisions possible—here, ‘outsiders’ aren’t those who don’t understand, but rather ‘valuable allies’ who bring entirely new perspectives.”

 3.2 From “Attending Lectures” to “Taking Home Solutions”: Unveiling High-Quality Workshops

 In today’s era of conference overload, most attendees share a common frustration: “I listened to countless lectures and took copious notes, but when I returned to the lab, I found nothing applicable.”The traditional “one-way output” model—experts lecturing on stage while attendees take notes below—no longer meets the practical needs of drug R&D professionals. SLAS2026 workshops completely disrupt this paradigm: they aren’t about “listening to lectures,” but “hands-on practice”; not about “acquiring information,” but “taking away solutions”; not about “passive reception,” but “active creation.”

 The core value of these high-quality workshops lies in their “practicality” and “interactivity.” Led by frontline R&D leaders and technical experts, they focus on specific development pain points. Through a comprehensive design combining “theoretical explanation + case analysis + hands-on practice + group discussion,” participants gain direct access to actionable technical solutions and tool templates within 6-8 hours—even completing preliminary project designs for their own initiatives.According to SLAS 2025 attendee feedback, 92% of workshop participants reported being able to “apply what they learned within one month of returning to the lab,” while 78% stated that “the workshop’s value far exceeded that of a typical lecture.”

 I. Core Features of SLAS2026 Workshops: Why Do They Deliver Actionable Solutions?

 SLAS2026 workshops achieve this leap from “listening to taking solutions” through four core characteristics—fundamentally distinguishing them from traditional conferences’ “pseudo-workshops” (which merely add interactive elements without practical hands-on):

 1. Pain-Point Focus: Each workshop tackles one specific R&D challenge

 Unlike conventional workshops that “vaguely discuss technological trends,” each SLAS2026 workshop focuses on one “specific, actionable” R&D pain point—selected through extensive industry research. For example:

•  High-Throughput Stability Screening for ADC Drugs: From Protocol Design to Data Interpretation: Addresses the pain points of “low efficiency in ADC stability screening and difficulty in data interpretation”;
•  “AI-Driven Small Molecule Optimization: Practical Tools and Case Studies”: Addresses the challenge of “inability to utilize AI models and unclear optimization directions”;
•  Laboratory Digital Transformation: LOS System Selection and API Integration Practical Guide: Addresses the pain points of “incompatible equipment and fragmented data”;
•  Off-Target Effects Detection for Nucleic Acid Therapeutics: Whole-Genome Screening Protocol Design: Addressing the challenges of incomplete off-target detection and lengthy timelines.

 This “single-point breakthrough” design allows attendees to concentrate on learning a complete solution for a specific problem, rather than dispersing their focus across a flood of unrelated information. Each workshop has a clear objective: attendees should be able to directly apply the learned solution to their own projects upon completion.

 2. Hands-on Focus: 70% Time Dedicated to “Doing,” Not “Listening”

 SLAS2026 workshop timelines adhere to a “30% theory + 70% hands-on” principle—theoretical instruction focuses solely on “core logic” and “critical steps,” omitting redundant background knowledge. The remaining time is entirely dedicated to “hands-on practice,” including tool usage, protocol design, and data interpretation.

 Taking the workshop “AI-Driven Small Molecule Optimization: Hands-On Tools and Case Studies” as an example, the schedule is as follows:

•  0-1 hour: Theory presentation (core logic of AI optimization, comparison of common tools, key parameter settings);
•  1-3 hours: Hands-on tool practice (using open-source AI platform ChemBL + RdKit to optimize the metabolic stability of a real compound on-site);
•  3-5 hours: Case study (group optimization of real candidate compounds provided by a pharmaceutical company, with expert on-site guidance);
•  5-6 hours: Solution presentation (each group presents optimization strategies, with expert feedback and improvement suggestions).

 During hands-on sessions, participants directly utilize industry-standard tools (e.g., AI platforms, data analysis software) and access real R&D data (de-identified), ensuring learned concepts translate directly to practical work.For instance, the ChemBL+RdKit platform used in the workshop is an open-source tool employed by 80% of global pharmaceutical companies. The optimized compound cases originated from a pharmaceutical company’s actual R&D pipeline, offering significant reference value.

 3. Expert Mentorship: Hands-on guidance from frontline R&D leaders, not just PowerPoint presentations

 The instructors for the SLAS2026 workshops are neither “academic experts who only lecture on theory” nor “salespeople focused solely on promoting products.” They are frontline R&D leaders from pharmaceutical companies, technology firms, and research institutions, each with an average of over 10 years of hands-on experience and currently spearheading projects in their respective fields.

 Their teaching approach emphasizes hands-on guidance over one-way lectures:

•  During hands-on sessions, experts circulate to provide personalized advice addressing each participant’s specific challenges (e.g., “Your AI model parameters are set too high, causing overfitting in optimization results”; “For this compound’s structural modification, consider introducing fluorine atoms to enhance metabolic stability”).
•  During group discussions, experts join each team to participate in solution design and resolve disagreements (e.g., “From a clinical translation perspective, the first optimization direction you proposed is more feasible”);
•  During the proposal review session, experts draw on their project experience to identify potential risks and areas for improvement in the proposals (e.g., “This screening approach overlooks compound solubility issues, which may prevent drug efficacy in vivo”).

I attended the “ADC Drug Screening Protocol Design” workshop in 2024, led by a Senior Director of ADC R&D at Genentech. While guiding us in designing screening protocols, she directly shared Genentech’s “lessons learned” from developing T-DXd:We once overlooked the stability of the linker in acidic environments, resulting in insufficient payload release efficiency during preclinical trials. We later resolved this issue by adjusting screening conditions and adding stability testing at pH 5.0.” This frontline practical experience is something no textbook or report can provide.

 4. Toolkit Output: Take-home templates, code, and workflow documentation

 The ultimate goal of the SLAS2026 workshops is for attendees to “take away actionable solutions”—hence, each workshop provides a comprehensive “toolkit” including:

•  Solution Templates: e.g., “ADC Stability Screening Template,” “AI-Driven Compound Optimization Workflow Template,” “LOS System Selection Evaluation Form”;
•  Tool resources: e.g., open-source software installation packages, API documentation, data analysis code (Python/R scripts);
•  Reference Materials: Industry standards, case study reports, expert experience summaries;
•  Follow-up support: Expert contact information, online Q&A groups, and tool update notifications.

 For example, the toolkit for the workshop “Laboratory Digital Transformation: LOS System Selection and API Integration Practical Guide” includes:

•  Selection Evaluation Form: Covers 20 assessment dimensions including core functionality, compatibility, compliance, and cost for LOS systems, enabling direct comparison of different brands;
•  API Integration Code Templates: Ready-to-use Python integration code for API interfaces of mainstream devices like Agilent, Thermo Fisher, and Beckman, requiring only minor parameter adjustments;
•  Compliance Checklist: Outlines key compliance verification points post-integration based on FDA 21 CFR Part 11 requirements, ensuring data traceability and auditability;
•  Online Q&A Group: Attendees can join a dedicated support group staffed by course instructors and equipment manufacturer technicians for ongoing integration assistance.

 According to SLAS 2025 statistics, 85% of workshop participants stated that “the toolkit accounted for over 40% of the workshop’s total value,” while 60% reported “directly using the toolkit to complete project design upon returning to their labs.”

 II. In-Depth Analysis of SLAS2026 Core Workshops: From Content Design to Practical Value

 SLAS2026 features 32 workshops covering core domains such as AI-driven drug discovery, ADC and nucleic acid drug development, laboratory digitization, and high-throughput screening. Below is an in-depth analysis of the four most anticipated workshops, offering a preview of their content design and practical value:

 1. Workshop: “Practical Implementation of Self-Driving Laboratory Setup: From Hardware Selection to AI Model Deployment”

•  Core Pain Points: Enterprises aim to establish autonomous labs but face challenges in hardware selection, AI model integration, and compliance resolution.
•  Target Audience: R&D leaders, lab managers, and technical procurement personnel in pharmaceutical companies;
•  Content Design:
•  Theory Module (1.5 hours): Core architecture of autonomous labs, hardware selection principles (compatibility of robotic arms, screening instruments, and sensors), integration logic between AI models and equipment, FDA compliance requirements;
•  Hands-on Module (4 hours):
•  Hardware Selection Exercise: Group comparison of robotic arms (e.g., Boston Dynamics, Beckman) and screening instruments (e.g., Agilent, PerkinElmer). Using provided evaluation sheets, develop a hardware configuration plan aligned with individual budgets and requirements;
•  AI Model Deployment Practice: Train a simple experimental optimization model using open-source reinforcement learning frameworks (e.g., Stable Baselines³) in a simulated environment. Integrate it with a virtual screening instrument to achieve a closed-loop process of “data collection → model learning → experimental adjustment”;
•  Compliance Design: Develop data traceability workflows for autonomous labs based on FDA requirements, ensuring auditable experimental operations, data logging, and model decision-making;
•  Solution Presentation (1.5 hours): Each group presents their autonomous lab setup plan. Experts provide feedback and improvement suggestions.
•  Toolkit Contents:
•  Hardware Selection Evaluation Sheet (includes 25 evaluation dimensions and weightings);
•  AI model deployment code template (Python script);
•  Self-Driving Lab Compliance Process Template;
•  Mainstream Equipment Compatibility List (API interface compatibility for brands like Agilent, Thermo, Beckman, etc.);
•  Deliverable Value: Attendees can directly utilize the toolkit to complete preliminary design plans for self-driven labs within 3 months, avoiding common issues like hardware incompatibility and compliance failures. This saves at least 6 months of research time and $500,000 in trial-and-error costs.

 2. Workshop: “Optimizing Nucleic Acid Drug Delivery Systems: LNP Formulation Design and High-Throughput Screening”

•  Core Pain Points: Lengthy LNP formulation optimization cycles, low screening efficiency, and difficulty identifying optimal solutions achieving “high encapsulation rate + high delivery efficiency + low toxicity.”
•  Target Audience: Nucleic acid drug developers, materials scientists, laboratory technicians;
•  Content Design:
•  Theory Module (1 hour): Mechanisms of core LNP components (ionizable lipids, cholesterol, PEG lipids), key formulation optimization parameters (component ratios, preparation processes), core metrics for high-throughput screening;
•  Practical Module (5 hours):
•  Formulation Design: Utilize LNP formulation design software (open-source tool provided by Precision NanoSystems) to design 100 candidate formulations based on target nucleic acid molecule (siRNA/mRNA) characteristics;
•  Virtual Screening: Utilize AI models (prediction tools provided by BenevolentAI) to forecast encapsulation efficiency, cellular uptake efficiency, and toxicity for candidate formulations, selecting 20 optimal formulations;
•  Experimental Validation: Conduct in vitro cellular uptake assays (using HeLa cells) for the 20 formulations on the workshop’s high-throughput screening platform, with real-time data monitoring and analysis;
•  Formulation Optimization: Adjust formulation parameters based on experimental results to establish the final optimized formulation;
•  Solution Output (1 hour): Each group presents optimized LNP formulations and experimental data; experts provide feedback and recommend in vivo testing;
•  Toolkit Contents:
•  LNP formulation design software installation package and user manual;
•  AI prediction model API interface and usage tutorial;
•  High-throughput screening data record template;
•  LNP formulation optimization case studies (including analysis of classic formulations from Moderna and BioNTech);
•  Deliverable Value: Attendees can directly apply designed LNP formulations to their own nucleic acid drug projects, reducing screening cycles from 45 days to 7 days, achieving encapsulation rates above 95%, and boosting cellular uptake efficiency by 3-5 times.

 3. Workshop: “AI-Driven Target Discovery and Validation: Practical Multi-Omics Data Integration”

•  Core Pain Points: Target discovery relies on single-source data (e.g., gene sequencing), resulting in low hit rates; multi-omics data integration is challenging, hindering effective identification of potential targets.
•  Target Audience: Bioinformaticians, drug target discovery researchers, AI engineers;
•  Content Design:
•  Theory Module (1 hour): Integration logic of multi-omics data (genomics, transcriptomics, proteomics, metabolomics); core algorithms of AI target prediction models (e.g., deep learning, graph neural networks); key experimental designs for target validation.
•  Practical Module (5 hours):
•  Data Preprocessing: Utilize Python libraries Pandas and Scikit-learn to clean, standardize, and extract features from real multi-omics data (de-identified);
•  Model Training: Utilize the TensorFlow framework to build a deep learning model integrating multi-omics data, train it, and predict potential targets;
•  Target prioritization: Rank predicted targets using provided evaluation tools, considering drugability and clinical value;
•  Validation Protocol Design: Develop in vitro validation protocols (e.g., CRISPR knockdown, Western Blot assays) for the top 3 targets;
•  Protocol Output (1 hour): Present predicted targets and validation protocols per group; experts provide feedback and improvement suggestions;
•  Toolkit contents:
•  Multi-omics data preprocessing code templates (Python scripts);
•  AI target prediction model setup tutorial and code;
•  Target drugability assessment worksheet;
•  In vitro validation experiment template;
•  Deliverable Value: Attendees can directly utilize the toolkit to complete multi-omics data integration and target prediction within one month, achieving a target hit rate exceeding 30% (industry average: 10%) and avoiding R&D waste caused by blind screening.

 4. Workshop: “Post-Event Technology Implementation: ROI Analysis & Executive Reporting Strategy Design”

•  Core Pain Points: Identifying promising technical solutions post-conference but lacking strategies for executive reporting, ROI calculation, and technology adoption.
•  Target Audience: R&D leads, technology procurement personnel, team managers
•  Content Design:
•  Theory Module (1 hour): ROI calculation logic for technology adoption, key metrics executives prioritize (cost, efficiency, risk), and structured presentation design;
•  Practical Module (5 hours):
•  ROI Calculation Exercise: Using provided ROI tools, calculate the return on investment and payback period for a specific technology (e.g., autonomous labs, AI screening platforms) by integrating your company’s R&D cost and cycle data;
•  Presentation Design: Develop a technology adoption presentation template following the “Problem-Solution-Value-Risk-Action Plan” structure, incorporating data validation, comparative case studies, and implementation roadmaps;
•  Mock Presentation & Feedback: Each team selects a representative for a 5-minute mock presentation. Experts act as “bosses,” posing challenges and offering improvement suggestions (e.g., “What are the maintenance costs for this technology? Will additional specialized personnel be needed?”);
•  Solution Output (1 hour): Each group presents their ROI analysis report and proposal. Experts provide feedback and optimization suggestions;
•  Toolkit Contents:
•  ROI calculation Excel tool (with automated formulas);
•  Technology Introduction Presentation PPT Template;
•  Common Boss Questions and Response Scripts Checklist;
•  Technology introduction implementation roadmap template;
•  Deliverable Value: Attendees can directly utilize the toolkit to complete ROI analysis and design a reporting plan for technology introduction within one week, boosting approval rates to over 70% (industry average: 30%).

 III. SLAS2026 Workshop Participation Strategy: How to Select and Engage for Maximum Benefit?

 SLAS2026 offers numerous workshops covering a wide range of topics, most requiring advance registration (some popular workshops are already full during pre-registration). To select the most suitable workshops within limited time and maximize benefits, follow these 4 strategies:

 1. Prioritize “R&D Pain Points” Over “Trending Technologies”

 When selecting workshops, avoid blindly chasing “hot technologies” (e.g., AI, autonomous labs). Instead, align with your most urgent and critical R&D challenges. For example:

•  If your primary challenge is “low efficiency in ADC drug stability screening,” the “High-Throughput Stability Screening for ADC Drugs” workshop holds greater value than “AI Target Discovery.”
•  If you’ve identified a suitable technical solution but need guidance on presenting it to your supervisor, the “Post-Conference Technology Implementation: ROI Analysis and Supervisor Presentation Design” workshop is your top choice.

 Prepare a list of your lab’s top three core pain points beforehand. Then, filter workshops on the SLAS website that address these specific challenges. According to SLAS 2025 attendance data, participants who selected workshops based on “pain point priority” achieved implementation rates 2.5 times higher than those who chose based on “technology popularity.”

2. Prepare “personalized data” in advance to tailor hands-on exercises to your specific project

 Most workshops allow participants to bring relevant data from their own projects (e.g., compound structures, screening data, R&D cost data) for direct use during hands-on sessions. Preparing this “personalized data” in advance enables the solutions developed during the workshop to directly integrate with your project, significantly enhancing their practical value.

 For example: – For the “AI-Driven Small Molecule Optimization” workshop, prepare your project’s compound structures (SMILES format), current activity data, and metabolic stability data in advance.For the “ROI Analysis and Executive Reporting Design” workshop, gather your company’s R&D costs (e.g., labor expenses, equipment procurement costs) and project timeline data beforehand. This ensures that the optimization plans and ROI analysis reports you develop during the hands-on sessions can be directly applied to your projects without requiring subsequent revisions.

 3. Actively “Engage Deeply” Rather Than “Passively Observe”

 The workshop’s value lies in “interaction and hands-on practice,” so actively participate in all segments:

•  Hands-on sessions: Don’t fear making mistakes. Seek expert guidance promptly when encountering challenges—personalized coaching is the workshop’s most valuable component.
•  Group discussions: Actively share your perspectives and project experiences while listening to others’ ideas—similar R&D pain points across companies may yield solutions from peer insights;
•  Solution Presentations: Proactively seek opportunities to present your group’s work and receive expert feedback—their critiques and suggestions help uncover potential flaws in your approach, preventing pitfalls during implementation.

 During the 2023 “Nucleic Acid Drug Off-Target Effects Detection” workshop, I proactively shared a “false positive off-target effect” issue from my project. This earned me targeted guidance from the instructor—he recommended incorporating an “RNA-seq validation step” into our screening process and shared a detailed experimental protocol.After returning to the lab, we adjusted our screening workflow based on this protocol, reducing the off-target effect false positive rate from 25% to 5%.

 4. Rapid Implementation Post-Workshop: Pilot Testing Completed Within 2 Weeks

 After the workshop, don’t let toolkits and protocols “sit idle on your hard drive.” Instead, initiate a pilot project within two weeks—select a small project or a specific project phase, apply the protocols and tools learned in the workshop, and validate their feasibility.

 For example: – After attending the “Self-Driven Lab Setup Workshop,” select a simple screening process (e.g., small molecule compound activity screening) and use the hardware selection evaluation sheet to compare 1-2 equipment brands. – After attending the “LNP Formulation Design and High-Throughput Screening Workshop,” design 10 LNP formulations for one nucleic acid molecule and conduct in vitro cellular uptake experiments.

 The value of rapid piloting lies in:

•  Timely identification of issues in the approach, leveraging workshop follow-up support (e.g., online Q&A groups) to consult experts;
•  Validating the approach’s efficacy with pilot data to build evidence for future large-scale implementation;
•  Maintaining learning continuity to prevent “learning and forgetting.”

 According to SLAS 2025 follow-up surveys, participants who initiated pilot testing within two weeks post-workshop achieved an 80% implementation rate for their protocols. In contrast, those who started pilot testing three months later saw only a 35% implementation rate.

 IV. Core Differences Between Traditional Conferences/Lectures and SLAS2026 Workshops: A Value Gap at a Glance

 To visually demonstrate the value of the SLAS2026 workshop, I have compiled a core difference comparison table between traditional conference lectures and SLAS workshops:

Comparison Dimension	Traditional Conference Lectures	SLAS2026 Workshop	Value Gap
Core Objective	Deliver information, share trends	Addressing Specific Pain Points and Delivering Actionable Solutions	Shift from “passive reception” to “active creation”
Time Allocation	90% Theoretical instruction, 10% Interactive activities	30% theory, 70% hands-on practice	Hands-on focus for direct skill mastery
Instructors	Academic experts, sales professionals, industry analysts	Frontline R&D leads and technical experts (average 10+ years of hands-on experience)	Delivering frontline practical insights and pitfall avoidance strategies
Interaction Format	Q&A sessions (audience questions, expert answers)	Panel discussions, hands-on coaching, mock presentations, expert feedback	Personalized guidance for targeted problem-solving
Deliverables	Notes, PPT course materials	Actionable solutions, toolkits (templates, code, workflows), ongoing support	Take-home resources ready for immediate application
Implementation conversion rate	5%-10%	70%-80%	Implementation value increase 7-16 times
Time cost return	3-6 months (requires independent assimilation and research)	1-2 weeks (direct application of pilot solutions)	80% reduction in implementation time
R&D Cost Savings	No direct savings (provides conceptual framework only)	Average savings of $300,000–$800,000 (avoiding trial-and-error, boosting efficiency)	Direct reduction in R&D costs
Ongoing support	None (no interaction after lecture concludes)	Online Q&A group, expert contact information, tool update notifications	Ongoing troubleshooting during implementation

 Summary: Workshops—SLAS2026’s Most Valuable Session

 Among all SLAS2026 sessions, workshops deliver the highest return on investment—in just 6-8 hours, you gain actionable technical solutions, tool templates, personalized guidance from frontline experts, and even complete preliminary project designs. For drug R&D professionals, this “from lecture to takeaway solution” experience is unmatched by any other conference.

 SLAS2026 workshops are fundamentally about “empowering through industry expertise.” They translate R&D insights from top pharmaceutical and tech companies into replicable solutions and tools. This enables small-to-medium pharma firms, startups, and even newcomers to stand on the shoulders of giants in their research, avoid common pitfalls, and significantly boost R&D efficiency and success rates.

 If you’re grappling with a specific R&D challenge, if you want to quickly master hands-on methods for cutting-edge technologies, or if you aim to deliver tangible value to your company after the conference, then SLAS2026’s workshops are absolutely the core component you cannot afford to miss.As one R&D lead who attended the 2024 workshop remarked: “SLAS workshops aren’t just ‘listening to a lecture’—they’re ‘having an industry expert guide you step-by-step through a project.’ This investment is more worthwhile than any training program.”

 3.3 Rapid Catch-Up for Newcomers and Cognitive Refresh for Veterans

 SLAS2026 delivers tailored value for attendees at every career stage:For newcomers with 1-3 years of experience, it serves as an accelerated industry classroom, helping them build systematic industry knowledge, master core skills, and cultivate valuable professional networks within a week. For seasoned professionals with over five years of experience, it functions as a feast of ideas for cognitive refreshment, helping them break free from fixed mindsets, capture emerging technology trends, and expand collaboration boundaries.This personalized value proposition is precisely why SLAS2026 attracts tens of thousands of global professionals.

 I. “Crash Course” for Newcomers: Covering Three Years of Growth in One Week

 For newcomers entering the drug R&D field, the biggest pain point is “fragmented knowledge”—the disconnect between academic theories and real-world R&D, coupled with limited exposure to only specific project segments, leaving them lacking a systematic understanding of the industry landscape, technological trends, and core tools.SLAS2026 addresses this through comprehensive empowerment—from agenda design and interactive scenarios to resource provision—enabling newcomers to rapidly transform from students into professional practitioners within a week.

 1. Knowledge Catch-Up: Building a Systematic Industry Knowledge Framework

 What newcomers lack most isn’t “isolated knowledge points,” but a “systemic framework.” They know “ADC drugs are antibody-drug conjugates,” yet remain unaware of ADC development workflows, core technical bottlenecks, or industry competition dynamics. They recognize “AI can be applied to drug discovery,” but don’t understand where AI fits within the process, common tools, or current technical limitations.

 The SLAS2026 agenda offers newcomers a “one-stop” knowledge catch-up opportunity—comprehensively covering core drug development knowledge from foundational technical principles to cutting-edge trend analysis, from individual technical steps to complete R&D workflows, and from laboratory methodologies to industry business logic. For example:

•  Foundational Modules: “Core Processes and Metrics in Drug Development,” “Technical Principles of ADCs and Nucleic Acid Therapeutics,” “Fundamental Operation of Laboratory Automation Equipment”;
•  Technical Modules: Core Algorithms and Tools for AI-Driven Drug Discovery, Experimental Design and Data Interpretation in High-Throughput Screening, Foundational Logic of Laboratory Digital Transformation;
•  Industry Modules: “2026 Innovative Drug R&D Trend Report,” “Global Pharmaceutical R&D Pipeline Analysis,” “Compliance Requirements and Ethical Standards in Drug Development.”

 Most instructors for these sessions are seasoned industry experts skilled at explaining complex concepts using “accessible language + real-world case studies,” avoiding the “obscurity” common in traditional training.For instance, in the session “Core Algorithms and Tools for AI-Driven Drug Discovery,” the expert instructor bypassed intricate mathematical principles of deep learning. Instead, they used concrete examples like “how AI designs compound structures” and “how AI predicts drug activity” to help newcomers quickly grasp AI’s practical applications and core value in drug discovery.

 Beyond formal sessions, attendees can explore exhibition halls and engage with exhibitors to gain hands-on familiarity with mainstream industry tools and products. At Agilent’s booth, for instance, visitors can operate the basic functions of a high-throughput screening instrument. At Recursion’s booth, demonstration videos offer an intuitive glimpse into the workflow of an autonomous lab.This “theory + practice” approach helps newcomers rapidly build a systematic industry knowledge framework, avoiding fragmented understanding.

 According to SLAS 2025 attendee feedback, 90% of newcomers reported gaining “clear understanding of the overall drug development process” post-conference, while 85% stated they “gained clarity on how their current role fits within the broader R&D workflow.”A 2024 attendee (a lab technician with one year of experience) shared: “Before, I only knew how to operate the screening instrument daily without understanding why or what the results meant. After SLAS, I grasped that our data aims to identify potential active compounds and learned how to evaluate compound quality from data—now my work feels more purposeful.”

 2. Skill Building: Mastering Core Tools and Methods for Immediate Application

 What newcomers crave most in the workplace is the ability to “hit the ground running”—they need to master industry-standard experimental methods, data analysis tools, and process protocols to avoid the awkwardness of being “theoretically knowledgeable but practically inept.” SLAS2026 enables newcomers to acquire at least 3-5 core skills within a week through workshops, hands-on demonstrations, and toolkit provision, ready for immediate application.

 For instance, addressing core needs, SLAS2026 offers multiple “entry-level” workshops:

•  Hands-On Operation and Maintenance of Common Laboratory Automation Equipment: Covers foundational devices like sorters, robotic arms, and microplate readers, explaining operational workflows, troubleshooting common issues, and daily maintenance methods. Newcomers gain hands-on experience with equipment to master core competencies.
•  Fundamentals of R&D Data Analysis: Excel & Python Basics: Covers organizing, analyzing, and visualizing R&D data, including advanced Excel functions and basic operations with Python’s Pandas library. Newcomers practice with real screening datasets.
•  Experimental Protocol Design and Data Recording Standards: Covers designing sound experimental protocols, standardizing data recording (GMP-compliant), and drafting experimental reports. Provides ready-to-use templates for newcomers.

 These workshops are characterized by “low entry barriers and high practicality”—instructors start from the most fundamental operations, avoiding complex jargon to ensure newcomers can understand and master the skills.For example, in the “Excel and Python Basics” workshop, instructors start with “importing screening data into Excel” and “using the VLOOKUP function to match data,” progressing step-by-step to “plotting activity data curves with Python” and “performing statistical analysis.” Detailed demonstrations accompany each step, allowing newcomers to follow along hands-on.

 Beyond workshops, attendees gain access to extensive “entry-level” resources from exhibitors. Thermo Fisher provides the “Laboratory Technician Operations Manual,” detailing fundamental procedures like cell culture, sample preparation, and detection analysis. Agilent offers complimentary online course accounts, enabling continued learning in equipment operation and data analysis techniques after the event.

 According to SLAS 2025 statistics, 78% of newcomers reported that “skills acquired at the conference were directly applicable to their work,” while 65% noted “over a 30% increase in work efficiency.”A 2023 attendee (a two-year R&D assistant) shared: “After the workshop, I learned to process and filter data using Python. Tasks that previously took two days to complete now take only half a day. Moreover, I mastered standardized experimental protocol design—my proposals, which were constantly sent back for revisions before, now pass on the first try.”

3. Networking Catch-Up: Cultivating High-Quality Connections That Will Accompany Your Career

 For newcomers, professional networks are not only “future collaboration resources” but also “current growth mentors”—they need guidance from industry veterans and exchanges with peers to quickly adapt to the workplace and clarify their development direction. SLAS2026 provides newcomers with a “zero-pressure” environment for building connections, allowing them to easily engage with industry experts, seasoned professionals, and fellow newcomers.

 SLAS2026 features multiple “newcomer-exclusive” networking opportunities:

•  Newcomer Welcome Reception: Held on the first evening of the conference, featuring industry veterans as “mentors” for face-to-face discussions, addressing questions about career development and technical learning.
•  Newcomer Breakout Sessions: Each group comprises 1-2 senior experts and 10-15 newcomers, discussing topics like “How Newcomers Can Grow Quickly” and “Common Workplace Pitfalls to Avoid”;
•  Mentor Matching Program: Newcomers can schedule one-on-one meetings with industry experts via the conference website. Each expert provides personalized career guidance to 3-5 newcomers.

 These designed scenarios lower the “barrier” for newcomers to engage with experts—free from the formality of formal meetings or the transactional nature of business negotiations, they foster genuine peer-to-peer sharing.For instance, at the 2024 Newcomer Welcome Reception, a senior R&D director from Vertex Pharmaceuticals shared his career journey: “When I started, I was just an ordinary lab technician. By continuously learning automation techniques and AI tools, I gradually grew into a project lead. You’re entering a better era with more technical resources available—keep your passion for learning alive and don’t limit yourself to your current role.”

 Beyond expert resources, newcomers also connect with peers from diverse companies and regions who share similar growth challenges and work dilemmas.Through these exchanges, participants can share work experiences, swap learning resources, and build lasting connections. For instance, a 2025 attendee met three other newcomers from different pharmaceutical companies during a group discussion. Afterward, they formed an online study group to regularly share industry insights, technical materials, and work reflections, growing together.

 According to SLAS’s 2025 follow-up survey, 70% of newcomers reported “building over 10 high-quality professional connections” after attending, while 60% stated these connections provided “tangible support in subsequent work” (e.g., resolving technical issues, recommending learning resources, or facilitating job referrals).

 4. Core Value of Newcomer Participation: Data Demonstrates Growth

 To more vividly demonstrate the core value of newcomers attending SLAS2026, I have compiled the following comparative data table (based on feedback statistics from new attendees at SLAS2023-2025):

Growth Dimension	Pre-Conference (Newcomer Average)	3 Months Post-Event (Newcomer Average)	Growth Rate
Industry Knowledge Comprehensiveness	30% (understands only a single aspect)	85% (Mastery of complete R&D process and trends)	183%
Number of Core Skills Mastered	2-3 items (basic lab operations)	8-10 items (including data analysis, solution design, tool usage)	233%-400%
Work Efficiency	Completion of 1 task requires 8-10 hours	Completion of 1 task requires 4-5 hours	100%
Approval rate	30%-40% (Frequently rejected by management)	75%-80% (meets specifications and requirements)	100%-167%
Number of professional contacts	5-10 people (colleagues + alumni)	30-50 people (experts + peers + newcomers)	300%-500%
Career Advancement Opportunities	Low (promotion probability within 1 year <10%)	High (promotion probability >30% within 1 year)	200%

 II. Refreshing the Mindset of Experienced Professionals: Breaking Boundaries to Unlock New R&D Possibilities

 For seasoned professionals with over five years in the field—such as R&D leads, technical experts, and team managers—systematic industry knowledge and proficient core skills are established. Yet they now face new challenges: “Cognitive Rigidity”—prolonged focus on a single domain gradually limits thinking patterns and technical vision; “Intensified Competition”—accelerating industry technology iteration demands constant tracking of emerging trends and opportunities;and “limited collaboration”—where professional networks remain confined to their niche, hindering cross-industry partnerships. SLAS2026 addresses these challenges through comprehensive empowerment: cutting-edge trends, cross-disciplinary exchanges, and collaborative matchmaking. This enables seasoned professionals to achieve a “cognitive refresh” within a week, unlocking new frontiers in R&D.

 1. Cognitive Refresh: Shatter Fixed Mindsets, Capture Technological Trends

 What seasoned professionals need most isn’t “new knowledge,” but “new perspectives.” They are familiar with traditional R&D logic but need to understand how cutting-edge technologies are reshaping development processes. They master their field’s methodologies but require insights into how cross-disciplinary technologies solve domain-specific challenges. They grasp the current industry landscape but must anticipate technological trends over the next 3-5 years.

 SLAS2026’s cutting-edge agenda delivers a feast of ideas for cognitive refreshment—focusing on the industry’s most groundbreaking advancements, disruptive R&D paradigms, and forward-looking trend assessments. This empowers seasoned professionals to break free from conventional thinking and re-examine core challenges in drug discovery. For example:

•  Technology Trends Module: “The 2030 Technical Blueprint for Drug Discovery,” “Next-Generation R&D Models: AI + Synthetic Biology,” “Quantum Computing Applications in Drug Design”;
•  Cross-Disciplinary Integration Module: “Microfluidics + AI: Revolutionizing Single-Cell Analysis,” “The Intersection of Materials Science and Immunology: Next-Generation Vaccine Delivery Technologies”;
•  Business Logic Module: Differentiated Competitive Strategies for Innovative Drugs, Collaborative Model Innovations Between Startups and Giants, Resource Integration Logic for Global R&D.

 Most instructors for these sessions are industry thought leaders—they may be CTOs of AI giants, R&D directors at top pharmaceutical companies, or professors at renowned research institutions. They not only share technological trends but also convey underlying conceptual frameworks.For instance, in the session “The 2030 Roadmap for Drug Discovery,” a senior researcher from Google DeepMind shared: “Future drug discovery will no longer be about ‘screening compounds,’ but ‘designing disease solutions’—AI will integrate multi-omics data, clinical data, and environmental data to directly design personalized treatment plans for individual patients, with drugs being just one component.” This fundamental shift in thinking holds far greater value than understanding any single technology.

 Beyond formal agendas, seasoned professionals can uncover “unannounced” technological trends through deep exchanges with industry experts. For instance, during informal settings like coffee breaks or dinners, experts often share insights on “next-generation technologies” they’re researching or “potential breakthrough points” within the industry—information that frequently holds greater foresight than public agendas.At the 2024 SLAS dinner, I spoke with an MIT professor who shared a technical direction combining CRISPR-based gene editing with AI prediction. At the time, this technology had not yet gained widespread attention. Within a year, it became an industry hotspot, with multiple pharmaceutical companies rushing to develop it. This opportunity to “capture trends early” is precisely the value most valued by seasoned professionals.

 According to SLAS 2025 attendee feedback, 88% of senior professionals reported “gaining clearer insights into future industry trends” post-conference, while 76% stated it “challenged conventional R&D thinking and uncovered new technical directions.”A senior R&D leader from Pfizer (with 12 years of industry experience) wrote in their feedback: “Our previous R&D mindset was ‘from target to drug,’ but after attending SLAS, I realized the future approach should be ‘from patient needs to solutions.’ This shift in thinking prompted us to adjust our company’s R&D pipeline strategy, adding projects focused on ‘precision medicine based on patient individuality.'”

 2. Collaboration Refresh: Expanding Cross-Boundary Frontiers, Connecting Premium Resources

 The core responsibilities of seasoned professionals have shifted from “individual execution” to “team management and resource integration.” They must identify high-quality partners (such as technology providers, CROs, and research institutions), expand R&D resource boundaries (e.g., data, equipment, technology licensing), and drive cross-disciplinary collaborations. SLAS 2026 offers these professionals a “one-stop” collaboration platform, enabling them to connect with global premium resources within a single week.

 SLAS2026’s collaboration scenarios span multiple dimensions including “technical partnerships, commercial collaborations, and research alliances”:

•  Technology Collaboration Matching: Over 300 exhibitors in the hall include equipment manufacturers, AI technology providers, CRO companies, and data service providers. Senior professionals can directly engage with exhibitors’ technical leads and executives to explore collaboration models such as technology licensing and joint R&D.
•  Business Collaboration Matching: A dedicated “B2B Business Meeting Zone” offers scheduled one-on-one sessions. Attendees can engage with R&D leaders from pharmaceutical companies and investors from venture capital firms to explore project collaborations, pipeline licensing, and investment opportunities.
•  Research Collaboration Matching: The conference invites professors and researchers from world-leading institutions (e.g., MIT, Stanford University, Harvard University). Senior professionals can explore collaboration models such as integrating basic research with clinical applications and jointly applying for research projects.

 The value of these collaboration platforms extends beyond “quickly finding partners” to “securing high-quality cross-industry partners.”For instance, a senior technical expert (8 years in the field) from a startup pharmaceutical company secured a collaboration at SLAS 2024 with a firm specializing in microfluidic technology. Applying microfluidic chip technology to their CAR-T cell therapy drug screening reduced the screening cycle from 3 months to 2 weeks while enhancing accuracy. This project later secured $20 million in funding.

 Beyond formal collaboration platforms, experienced professionals can cultivate “hidden partnership resources” through informal exchanges. For instance, during a cross-disciplinary Q&A session, a technical challenge posed by one expert might be addressed by another specialist from a different field, potentially sparking future collaborations.At SLAS 2023, I witnessed a collaboration between an ADC R&D lead and a materials scientist. The former faced the challenge of “short in vivo circulation time for ADC drugs,” while the latter shared their team’s “long-circulation nanocarrier” technology. Their expertise clicked instantly, leading to the joint development of a novel “ADC + long-circulation carrier” drug currently in preclinical research.

 According to SLAS 2025 attendee feedback, 82% of senior professionals reported “securing at least one collaboration agreement post-conference,” 68% confirmed “collaborative projects materialized within six months,” and 55% noted “these partnerships delivered significant R&D efficiency gains or commercial value for their companies.”

 3. Management Refresh: Optimizing Team Strategies and Enhancing Leadership

 For senior managers (e.g., R&D directors, department heads), SLAS 2026 also offers an opportunity to refresh management capabilities—by learning from other companies’ team management models, technology introduction strategies, and talent development methods to optimize their own team management and enhance leadership.

 For instance, SLAS2026 features multiple management-focused sessions:

•  Efficient Management of Drug R&D Teams: From Goal Setting to Performance Evaluation: Shares top pharmaceutical companies’ team management models, including how to set R&D objectives, motivate team members, and evaluate performance.
•  “Decision Logic and Risk Control in Technology Licensing”: Explaining how to evaluate the feasibility of new technologies, calculate return on investment, and manage risks associated with technology licensing;
•  Talent Development and Team Building: Creating Innovative R&D Teams: Exploring strategies to attract and retain top talent, cultivate cross-disciplinary thinking among team members, and foster an innovative team culture.

 The instructors for these sessions are predominantly pharmaceutical executives and team leaders with extensive management experience, sharing “practically proven” management insights. For instance, in the “Decision Logic and Risk Control for Technology Introduction” session, a Vice President of R&D from Merck shared:When introducing new technologies, we follow the ‘small pilot, large rollout’ principle—first selecting a small project for testing to validate the technology’s feasibility and value, then gradually expanding it across the entire team to minimize risk. Simultaneously, we establish a ‘Technology Evaluation Committee’ comprising technical experts, finance personnel, and legal counsel to ensure scientifically sound decision-making.”

 Beyond agenda-based learning, senior managers can exchange insights and tackle management challenges through peer discussions. For instance, during the 2024 SLAS “Managers’ Closed-Door Session,” R&D directors from various pharmaceutical companies debated “How to Ignite Team Innovation,” sharing practical approaches: some companies established “Innovation Incentive Funds” to financially support valuable ideas;others implemented “flexible work arrangements” to free up time for independent research; and some built “internal technical exchange platforms” to encourage cross-team knowledge sharing. These frontline management insights help senior leaders refine their strategies, boosting team cohesion and innovation.

 4. Core Value of Senior Participation: Data-Driven Breakthroughs

 Below is a comparative data table highlighting the core value of senior professionals attending SLAS2026 (based on feedback statistics from senior attendees at SLAS2023-2025):

Breakthrough Dimension	Pre-Conference (Senior Average)	6 Months Post-Attendance (Senior Average)	Breakthrough Magnitude
Ability to Anticipate Technological Trends	60% (Can predict trends 1-2 years ahead)	90% (Can predict trends 3-5 years ahead)	50%
Cross-Industry Collaboration Opportunities	2-3 per year (primarily within own field)	8-10 per year (primarily cross-sector)	233%-400%
Project R&D Cycle	Average 18-24 months	Average 12-15 months	25%-33%
R&D success rate	Preclinical success rate: 20%-25%	Preclinical success rate: 35%-40%	75%-100%
Team Innovation Capability	2-3 innovative ideas implemented annually	6-8 innovative ideas implemented annually	200%-267%
Business Value Creation	Average annual value creation for the company: $5-8 million	Generates an average annual value of $15-20 million for the company	200%-250%

 III. How does SLAS2026 cater to attendees of varying experience levels?

 SLAS2026 meets the needs of both newcomers and seasoned professionals through its core “tiered design” philosophy—precisely aligning agenda, interactive scenarios, resource provision, and service support across levels:

 1. Tiered Agenda: Comprehensive coverage of foundational, advanced, and cutting-edge modules

 SLAS2026’s agenda is divided into three tiers, allowing attendees to choose based on their needs:

•  Foundational Modules: Designed for newcomers, focusing on technical principles, basic operations, and industry fundamentals to help build foundational knowledge frameworks;
•  Intermediate Modules: Designed for experienced practitioners, emphasizing hands-on technical skills, solution design, and problem-solving to enhance professional capabilities;
•  Frontier Modules: Designed for senior professionals, emphasizing technological trends, cross-industry integration, and business logic to refresh perspectives and expand boundaries.

 For example, under the theme “AI-Driven Drug Discovery,” the agenda includes:

•  Foundational Module: “Introduction to AI-Driven Drug Discovery: Core Concepts and Essential Tools”;
•  Intermediate Module: AI-Driven Compound Optimization: Practical Case Studies and Parameter Tuning;
•  Frontier Module: Next-Generation Applications of Generative AI in Drug Discovery: From Target Identification to Clinical Trials.

 This tiered design ensures attendees at all levels find relevant content, avoiding the pitfalls of “newcomers feeling lost and veterans finding it too basic.”

 2. Tiered Scenarios: Dedicated Newcomer, Expert-Only, and Mixed Interaction Scenarios

 SLAS2026 features three types of interactive scenarios to meet diverse networking needs:

•  Newcomer-Exclusive Scenarios: Welcome receptions, mentor matching programs, and foundational skill workshops reduce social pressure for newcomers and facilitate rapid integration.
•  Experts-Only Scenarios: Such as executive closed-door meetings, cutting-edge technology roundtables, and B2B collaboration zones, providing deep networking and partnership opportunities for seasoned professionals;
•  Hybrid interaction scenarios: Theme dinners, coffee corner exchanges, and innovation challenges enable free interaction between newcomers and veterans, achieving mutual empowerment through “experience transfer” and “sparking inspiration.”

 3. Tiered Resource Allocation: Precision-targeted entry-level, advanced, and premium resources

 SLAS2026 resources are tiered as follows:

•  Entry-level resources: Designed for newcomers, including basic operation manuals, tool installation packages, and introductory course accounts to help them get started quickly;
•  Intermediate Resources: For experienced practitioners, including practical code templates, solution design tools, and industry standard documentation to enhance work efficiency;
•  Premium Resources: Targeted at senior professionals, including cutting-edge technology white papers, partnership matching databases, and one-on-one expert consultation opportunities to connect them with premium resources.

 Summary: SLAS2026—A Growth Accelerator for Professionals at Every Level

 Whether you’re a newcomer or a seasoned professional, SLAS2026 delivers tailored value: For newcomers, it’s an industry classroom for “rapid catch-up,” helping them cover three years of growth in just one week. For veterans, it’s a “mind-refreshing” feast of ideas, breaking through established boundaries to unlock new R&D possibilities.This personalized value proposition is SLAS2026’s unique appeal—it transcends a technical conference to become an industry ecosystem where every attendee discovers their growth path, collaboration opportunities, and future direction.

 As SLAS2026 Conference Chair Jennifer Cook stated in her opening address: “SLAS’s value lies in accompanying every professional throughout their career journey—from the uncertainty of entry to breakthroughs as an industry expert; from first hearing about ‘self-driven labs’ to leading your team in building your own system. Here, you will always find value tailored to your needs and glimpse the next frontier of drug discovery.”

IV. Masterclass: How to Maximize Your Week at SLAS2026 & bio conference boston 2026?

 SLAS 2026 delivers an “information density peak experience”—over 300 exhibitors, 500+ events, and tens of thousands of industry professionals. Each day presents dozens of session choices and hundreds of potential networking opportunities.Without a strategic approach, you risk becoming overwhelmed and underwhelmed: getting lost in the schedule, engaging in awkward networking conversations, or aimlessly wandering the exhibition halls.

 This chapter’s “Masterclass for Attendees” distills 15 years of conference experience into a practical guide—skipping abstract “mindset adjustments” for actionable strategies. From agenda filtering and networking to exhibition hall exploration, each step features concrete methods, case studies, and data validation. This empowers you to capture maximum core value with minimal effort during this high-intensity week.

 4.1 Agenda Management 2.0: Strategy Selection Based on “Pain Point Tags”

 Traditional agenda selection often falls into two traps: either “following the crowd” (attending whatever others are attending) or “overcommitting” (packing schedules to the brim). The result is typically “hearing a lot, remembering little, and applying even less.”According to SLAS 2025 attendee research, 73% of participants reported attending “at least three ineffective sessions.” These sessions either failed to address personal needs or offered vague content lacking practical value, squandering precious conference time.

 The core logic of Agenda Management 2.0 is “pain-point orientation”—first clarify your core needs, then use “pain-point tags” to precisely match sessions, rejecting “aimless attendance.” Its core steps break down into four phases: “Define Pain Points → Convert Tags → Filter Matches → Prioritize,” taking no more than 2 hours total. This boosts your session hit rate from 30% to 85%.

 I. Step 1: Define Core Pain Points—Identify the “Top 3 Most Critical Issues”

 Before opening the SLAS agenda list, dedicate one hour to identifying the three most urgent and critical challenges facing your lab or project. The key here is specificity and actionability—avoid vague terms like “technical enhancement” or “efficiency optimization.”

 The “SMART Principle” for Defining Pain Points

•  Specific: Instead of stating “ADC drug development has issues,” specify “The linker in ADC drugs exhibits insufficient stability in the bloodstream, with a 30% payload detachment rate within 24 hours.”
•  Measurable: Instead of “low screening efficiency,” state “small molecule compound screening takes up to 6 weeks; reduce cycle time to under 2 weeks”;
•  Actionable: Instead of saying “AI models are ineffective,” state “AI-predicted compound activity deviates from experimental results by 40%, requiring improved model accuracy”;
•  Relevant: Focus on projects currently under your responsibility or upcoming tasks, rather than “potential future issues”;
•  Time-bound: Specify a resolution timeline, e.g., “LNP formulation optimization must be achieved within 3 months.”

 Practical Example: Pain Point List for an Oncology R&D Team

Priority of Pain Points	Specific Pain Point Description (SMART-compliant)	Desired Solution
1 (Highest)	Insufficient stability of the linker in ADC drugs, with a 30% payload detachment rate within 24 hours in the bloodstream, leading to increased off-target toxicity	Automated solution for linker screening, key stability metrics and testing methods
2	Low efficiency in single-cell data analysis: processing 100,000 cells requires 24 hours, with only 40% effective signal extraction rate	High-efficiency single-cell data analysis tools and practical workflows for AI-assisted signal extraction
3	Laboratory equipment data lacks interoperability; data from sorters, mass spectrometers, and LIMS systems requires manual re-entry, resulting in a 12% error rate	LOS system selection criteria and API integration solutions for different brands of equipment

 Step 2: Translate Pain Points into Tags—Crafting “Precision-Targeting Keyword Combinations”

 Transform each pain point into a “core keyword + extended keyword” tag combination—the key to interfacing with website filtering functions. Core keywords represent the essence of the pain point, while extended keywords cover related technologies, tools, and processes, ensuring no potential solutions are overlooked during filtering.

 Pain Point Label Conversion Example

Core Pain Point	Core Keywords	Extended Keywords	Complete Tag Combination
Insufficient Stability of ADC Connectors	ADC, Linker, Stability	Automated Screening, Circulation, Off-Target Toxicity, UPLC-MS Detection	ADC + Linker + Stability + Automated Screening, ADC + Linker + Circulation + Off-Target Toxicity, ADC + Stability + UPLC-MS Detection
Low Efficiency of Single-Cell Data Analysis	Single-cell data, data analysis	AI-assisted, signal extraction, Python tools, high-content imaging	Single-cell data + AI-assisted + signal extraction, Single-cell data analysis + Python tools, High-content imaging + data processing
Lack of interoperability between devices	Device interconnectivity, data interoperability	LOS system, API integration, standardized interfaces, OPC UA	Device Interconnection + LOS System, Data Interoperability + API Integration, Standardized Interface + OPC UA

 III. Step Three: Screening and Matching—Using the Official Website Tool for “Precision Fishing” of High-Value Agendas

 The agenda filtering function on the SLAS2026 official website has been upgraded to “multi-tag combination filtering,” supporting multi-dimensional filtering by keywords, speakers, time, topic type, and more. Specific steps are as follows:

 1. Basic Filtering: Keyword Combination Search

•  Log in to the SLAS official website, navigate to the “Agenda” page, and enter a complete tag combination in the search box (e.g., “ADC+Connectivity+Stability+Automated Screening”);
•  Select “Session Type” (Presentation, Workshop, Roundtable), prioritizing “Workshop” and “Technical Session” for maximum practical value;
•  Filter out sessions with time conflicts and add preliminary selections to “My Schedule.”

 2. In-depth Screening: 3 Key Metrics for Evaluating Agenda Quality

 After initial screening, you may have 10-15 candidate sessions. Further evaluate their quality to avoid pitfalls. Focus on these 3 core metrics:

• Speaker Background: Prioritize “frontline R&D leaders” (e.g., Senior Scientist or Director at pharmaceutical companies) and “hands-on technical experts” (e.g., Application Scientist at equipment manufacturers) over purely academic experts or sales personnel. Click on speaker names to view their LinkedIn profiles and verify relevant project experience.
•  Agenda Description Details: High-quality agenda descriptions include “specific case studies,” “practical methodologies,” and “data-backed insights,” such as “5 Key Steps to Enhance ADC Linker Stability via Automation Platforms with Case Analysis.” Avoid selecting agendas with vague descriptions like “Future Trends in ADC Drug Development.”
•  Historical Reviews: Below each agenda item on the SLAS website, past attendee reviews appear (e.g., “Highly actionable content,” “Case studies were very valuable”). Prioritize sessions with ratings ≥4.5 out of 5.

 3. Final Matching: Create a “Core Agenda + Alternate Agenda” List

 Match each pain point with 2-3 core sessions (ensuring diverse solution perspectives) and 1-2 backup sessions (for overbooked or underwhelming core sessions). The final schedule should follow the “fewer but better” principle: select 3-4 core sessions daily, leaving ample gaps for networking, exhibit hall interactions, and reflection to avoid “rushing from session to session.”

 Practical Example: Final Agenda List for a R&D Team (Day 1)

Time	Core Agenda	Agenda Type	Matching Pain Points	Reason for Selection
09:00-10:30	High-Throughput Stability Screening of ADC Connectors: Automated Solutions and Data Interpretation	Technical Session	Pain Point 1	Speaker is the Head of ADC R&D at Genentech with hands-on experience in T-DXd; agenda includes automated screening workflows and UPLC-MS detection methods
11:00 AM – 12:30 PM	AI-Assisted Single-Cell Data Analysis: From Signal Extraction to Visualization Hands-On	Workshop	Pain Point 2	Hands-on workshop providing Python code templates; speaker is a senior data scientist at Google DeepMind
2:00 PM–3:30 PM	Standardized Pathways for Laboratory Equipment Interconnectivity: API Integration and LOS Selection	Panel Discussion	Pain Point 3	Roundtable featuring technical experts from Agilent and Thermo Fisher to gain insights on integrating equipment from different brands
4:00 PM – 5:30 PM	Automated Tools and Case Studies for LNP Formulation Optimization	Technical Session	Alternative	Indirectly related to Pain Point 3, this session explores cross-industry applications of automation technology to broaden perspectives.

 IV. Step Four: “Dynamic Adjustment” Strategy for Agenda Management

 SLAS agendas may undergo last-minute changes (e.g., speaker substitutions, content adjustments). Additionally, you may discover new pain points or high-value sessions during attendance. Therefore, reserve flexibility for “dynamic adjustments”:

•  Spend 15 minutes each morning reviewing agenda updates on the official website to confirm changes to core sessions;
•  After each session, spend 5 minutes evaluating its value: If the content falls short of expectations or lacks practical applicability, decisively skip similar alternative sessions and allocate that time to exhibition hall discussions or networking;
•  Leverage lunch breaks or coffee sessions to exchange insights with peers on “which sessions are worth attending,” gathering firsthand recommendations to supplement your schedule.

 V. Traditional Agenda Selection vs. Pain Point Tagging Method: Core Differences Compared

Comparison Dimensions	Traditional Agenda Selection Approach	Pain Point Tagging Method	Efficiency Improvement
Screening Time	8–10 hours (browsing all agendas)	2 hours (targeted screening)	75%
Agenda Match Rate (Relevance to Requirements)	30%	85%	183%
Percentage of ineffective agendas	40%-50%	10%-15%	70%-80%
Practicality of content	40% (theory-focused)	80% (Practical focus)	100%
Post-Event Implementation Rate	20%	70%	250%

 Summary: The core of agenda management—quality over quantity

 The essence of Agenda Management 2.0 is shifting from “passively receiving information” to “actively capturing value.” Through the four-step process—”Define Pain Points → Convert Tags → Filter Matches → Dynamically Adjust”—you can precisely pinpoint high-value content amidst a sea of agendas, avoiding wasted effort.Remember: At SLAS, it’s not about “attending as many sessions as possible,” but about “attending the right sessions.” Three core sessions precisely aligned with your pain points deliver far greater value than ten generic, trending sessions.

 4.2 Top Networking Tips: How to Make Industry Leaders Remember You?

 At SLAS 2026, networking holds value equal to any core session. The “big names” here—industry experts, R&D leaders from pharmaceutical companies, and technical gurus—could become your future collaborators, mentors, or even referral sources for your next job.Yet most attendees fall into two common pitfalls: either “not daring to speak up,” feeling flustered in front of big names; or “not knowing what to say,” resorting to generic lines like “I admire your work—can we exchange business cards?” which leave no lasting impression.

 The essence of top-tier networking isn’t “how many people you know,” but “how many people remember you”—especially those who can provide value. Below are three battle-tested core techniques covering Q&A sessions, in-person interactions, and follow-ups, transforming you from a “stranger” into a “professional worth deepening connections with.”

 I. Q&A Session: Become a “Rising Star” in the Eyes of Influencers with “Precision Questions”

 The Q&A session is a “low-cost, high-return” opportunity to connect with key players. Among dozens or even hundreds of attendees, only a few get to speak directly with them. Asking a deep, targeted question not only earns you a precise answer but also creates an initial impression of you as “professional and thoughtful.”

 The “Golden Formula” for Questions: Specific Scenario + Core Pain Point + Limited Options

 Effective questions aren’t “open-ended broad inquiries” but “focused, precise queries.” Using this golden formula framework allows experts to quickly grasp your question and provide targeted solutions.

•  Specific Context: Describe your current R&D background and project type (e.g., “Our team is developing an ADC drug for HER2-positive breast cancer”).
•  Core Pain Point: Describe your specific challenge (following the SMART principle);
•  Limited Options: Present 1-2 solutions you’re considering for the expert to evaluate or expand upon (avoid asking them to “start from scratch”).

 Negative Example vs. Positive Example

Question Types	Example	Expert Feedback
Negative (Open-ended, broad question)	“What do you think the future development trends are for ADC drug conjugates?”	Can only provide vague trend assessments, unable to delve deeper; completely forgotten after the call
Positive (Golden Formula Questioning)	“Our team is developing an ADC drug for HER2-positive breast cancer. We currently face a challenge: the linker exhibits a 30% payload detachment rate within 24 hours in the bloodstream, leading to increased off-target toxicity. We are considering two solutions: replacing the PEGylated linker or optimizing the conjugation process. From a clinical translation perspective, which approach do you consider more feasible? Or are there other optimization avenues we may have overlooked?”	Quickly get to the core issue and provide concrete suggestions; leave an impression of your project and expertise

 3 Practical Q&A Techniques

•  Prepare 2-3 questions in advance: For each keynote speaker, prepare 2-3 questions aligned with the golden formula beforehand to avoid nervousness or mental blocks during the session.
•  Seize the opportunity to ask questions: Raise your hand immediately after the session concludes (or be the first to type in the online Q&A channel), but avoid interrupting the expert’s summary.
•  Control question length: Keep each query under 30 seconds. Avoid lengthy project introductions—focus on “pain points + solutions.”

 Data-backed value of quality questions

 According to SLAS 2025 research, attendees who ask high-quality questions have a 63% chance of being remembered by speakers (who can recall their name or the core of their question during post-event networking). They also have a 28% chance of obtaining the speaker’s contact information—significantly higher than the 5% rate for casual acquaintances.

 II. Offline Networking: Building Lasting Connections Through Value Exchange

 Questions during Q&A sessions create a “first impression,” but true networking occurs after the agenda concludes—at coffee breaks, dinners, and other offline settings. The key here isn’t “seeking value” (e.g., “Can you help me solve this problem?”) but “value exchange”—making the other person feel that “knowing you benefits me too.”

 The 3-Step Icebreaker Method for Offline Interaction

•  Self-Introduction (10 seconds): Be concise and highlight your “role + core resources/expertise” (e.g., “Hello, I’m a R&D engineer at XX Pharmaceuticals, currently responsible for linker optimization in ADC drugs. Our team possesses extensive preclinical toxicity data”).
•  Relevant Question (30 seconds): Link to the speaker’s presentation or research focus with an extension question (e.g., “You mentioned earlier that your team used PEG-based linkers to keep detachment rates below 10%. We’re exploring similar approaches but encountering coupling efficiency issues—do you have optimization tips?”);
• Value Offer (20 seconds): Present the resources or assistance you can provide (e.g., “Our team has compiled toxicity data for various ligands. If you need it, I can organize and share it with you after the meeting”).

 Avoid 3 Networking Pitfalls

•  Pitfall 1: Overly promoting yourself or your company’s products—opening with “Our company’s equipment is amazing, would you like to learn more?” will create resistance.
•  Pitfall 2: Inquiring about private or sensitive information—questions like “How are your company’s ADC drug clinical data performing?” or “Who holds the patent rights for this technology?” are inappropriate;
•  Pitfall 3: Overstaying your welcome—Keep each interaction under 5 minutes. If the other person shows interest, suggest, “Can we schedule an online meeting later to discuss this further?” to avoid putting them under pressure.

 Practical Example: A Successful In-Person Exchange

 Attendee A (ADC R&D Engineer): “Hello, Dr. Smith. I’m Li Ming, an R&D engineer at XX Pharmaceuticals. I’m currently optimizing linkers for our HER2-positive breast cancer ADC drug. Our team has extensive preclinical toxicity data.Earlier you mentioned using PEGylated linkers to reduce shedding rates. After testing this approach, we observed a decrease in coupling efficiency from 90% to 75%. Could you share any optimization techniques? Additionally, we’ve compiled toxicity comparison data for 10 commonly used linkers. If your team requires it, I can organize and share this information after our meeting.”

 Dr. Smith (Genentech ADC R&D Lead): “Pleased to meet you, Li Ming.Regarding the coupling efficiency drop, we’ve encountered this before. The key lies in PEG chain length selection—I recommend trying a 5kDa PEG chain while adjusting the coupling reaction pH to 7.2. Your toxicity data is also highly valuable. I’m currently conducting a meta-analysis on linker toxicity and greatly need such data support. Here’s my business card. Feel free to email me after the meeting for further discussion.”

 III. Follow-Up: Strengthen Connections with Personalized Actions

 The true value of networking lies not in fleeting conference encounters, but in sustained follow-up afterward—a step most overlook, rendering prior efforts futile. According to SLAS 2025 research, only 15% of attendees follow up within 24 hours post-conference, yet these individuals achieve an 80% success rate in building lasting connections.

 The “Golden Window” for Follow-Up: Within 24 Hours After the Event

 The core of follow-up is “personalized, valuable, and non-intrusive.” Avoid sending generic thank-you emails (e.g., “It was great meeting you, looking forward to future collaboration”).

 Personalized Follow-Up Email Template (300 words max)

 Subject: Follow-up on our conversation about ADC linker optimization at SLAS 2026

 Dear Dr. Smith,

 It was a pleasure meeting you yesterday after the “High-Throughput Stability Screening of ADC Linkers” session. I truly appreciated your advice on using 5kDa PEG chains and adjusting the pH to 7.2 to address the conjugation efficiency drop—we will test this approach in our lab next week.

 As discussed, I’ve attached toxicity comparison data for 10 common linkers (including PEGylated and non-PEGylated types) from our preclinical studies. I hope this information proves useful for your meta-analysis on linker toxicity.

 Should you have any questions about the data or require additional support from our team, please don’t hesitate to reach out. Would a 15-minute virtual call next week be convenient to discuss the test results?

 Best regards,

 [Your Full Name]

 [Your Title]

 [Your Company]

 [Your Contact Information]

 Three Key Follow-Up Actions

•  Attach valuable resources: such as the toxicity data mentioned in the case study, your curated meeting notes, or relevant technical literature to demonstrate your sincerity;
•  Propose concrete next steps: e.g., “Schedule a 15-minute online meeting” or “Share test results,” avoiding vague phrases like “Stay in touch”;
•  Control follow-up frequency: If no response is received, send one reminder email after one week (keep it concise and avoid repeating previous content). If still no response, refrain from further contact.

 IV. Comparison Table of Networking Techniques for Different Scenarios

Scenario	Core Objective	Practical Techniques	Time Management	Follow-Up Actions
Q&A Session	Establishing an Initial Professional Impression	Golden Formula Questions: Highlight Pain Points + Options	30 seconds per question	Proactively introduce yourself after the meeting to confirm key recommendations
Post-agenda networking	Delve into specific issues	3-Step Icebreaker Method, Value Exchange	5 minutes per person	Exchange business cards and send personalized emails within 24 hours
Coffee Break/Dinner	Expand cross-industry connections	Start with shared topics (e.g., agenda items, industry trends), avoid overly technical discussions	10 minutes per person	Connect on LinkedIn, share relevant resources
Workshop groups	Build peer connections	Actively participate in discussions and share practical experience	Full-time interaction	Form discussion groups to regularly share progress

 Summary: The Essence of Networking—”Professionalism + Sincerity + Value”

 Top-tier networking isn’t about “currying favor with big names,” but rather making others feel “you’re someone worth deepening the relationship with” through “professional questions, sincere exchanges, and valuable contributions.”Remember: Influencers prefer engaging not with “admirers,” but with “professionals who offer fresh perspectives and resources.” By applying these techniques, you can build high-quality connections at SLAS2026 without forcing visibility.

 4.3 Exhibition Hall Exploration Guide: High-Throughput Screening Among 300+ Exhibitors

 The SLAS2026 exhibition hall spans over 50,000 square meters, featuring 300+ exhibitors representing the entire industry chain—from equipment manufacturers and AI technology providers to CRO companies and reagent suppliers. For attendees, the exhibition hall isn’t a place for casual browsing but a core venue for finding technical solutions and connecting with collaborative resources.

 Yet most attendees fall into an “inefficiency trap”: either getting bogged down in hour-long sales pitches yielding only brochures, aimlessly wandering past promising exhibitors, or fixating on big-name brands while overlooking innovative startups.

 The core logic of exhibition hall exploration is “high-throughput screening”—like sifting through compounds, it involves rapidly identifying quality exhibitors, precisely connecting with key contacts, and efficiently gathering core information to achieve “targeted capture” of over 300 exhibitors within 2-3 hours.

 I. Pre-Event Preparation: Building Your “Exhibitor Screening List”

 Before entering the exhibition hall, spend one hour preparing to avoid aimless wandering. The core is to create an “Exhibitor Screening List,” clearly defining “target exhibitors, key contacts, and core questions.”

 1. Step 1: Screen Target Exhibitors (≤30)

•  Core Exhibitors (10-15): Companies directly addressing your 3 core pain points, e.g., equipment suppliers for ADC conjugate stability or AI providers for single-cell data analysis.
•  Potential Exhibitors (10-15): Companies with innovative technologies indirectly relevant to your pain points, such as startup automation tool providers or cross-industry technical service providers;
•  Exclude: Pure reagent suppliers and exhibitors unrelated to your R&D focus.

 Screening Tool: SLAS Official Website’s “Exhibitor Directory”

•  Keyword search: Enter your pain point tags (e.g., “ADC linker stability,” “single-cell data analysis”) to filter relevant exhibitors;
•  Review exhibitor profiles: Focus on core products/services, client case studies (e.g., collaborations with major pharma companies), and technical strengths;
•  Mark booth numbers: Note target exhibitors’ booth locations on the hall map to plan your route (by zone sequence to avoid backtracking).

 Practical Example: Target Exhibitor List for a R&D Team

Exhibitor Type	Target Exhibitors	Core Products/Services	Matching Pain Points	Booth Number
Core Exhibitors	Agilent	ADC Automated Screening Platform, UPLC-MS Detection System	Pain Point 1 (Ligand Stability)	B101
Core Exhibitor	10x Genomics	Single-Cell Sequencing and Data Analysis Tools	Pain Point 2 (Single-Cell Data Analysis)	C205
Core Exhibitor	Thermo Fisher	LOS System, Equipment API Integration Services	Pain Point 3 (Lack of Interoperability Between Devices)	A302
Potential Exhibitors	Recursion	AI-Driven Autonomous Laboratory Solutions	Pain Points 1 & 2	D108
Promising Exhibitor	Precision NanoSystems	LNP Formulation Automation Optimization Platform	Pain Point 3 (Cross-Industry Application of Automation Technology)	B203

 2. Step Two: Identify the Right Contact—Seek “Application Scientists” Rather Than “Sales”

 Exhibitor personnel in the hall primarily fall into two categories: Sales and Application Scientists. For attendees, Application Scientists are the ones who truly deliver value—they possess frontline R&D experience, understand your technical pain points, and provide concrete solutions rather than merely promoting products.

 How to quickly identify application scientists?

•  Check name tags: Positions like “Application Scientist” or “Senior R&D Specialist” are typically listed.
•  Ask key questions: If unsure, directly inquire, “Is the Application Scientist responsible for technical solutions available? I’d like to discuss specific experimental protocols.”
•  Observe their behavior: Application scientists are usually found in the demonstration area of the booth, operating equipment or answering technical questions, rather than proactively approaching visitors to sell.

 Sales Reps vs. Application Scientists: A Comparison of Value

Comparison Dimensions	Sales	Application Scientist	Engagement Priority
Core Competencies	Familiarity with product specifications, pricing, and sales policies	Proficiency in technical principles, experimental protocols, and customer case studies	Application Scientist > Sales
Information Provided	Product brochures, pricing quotes	Specific solutions, practical techniques, data support	Application Scientist > Sales
Ongoing Support	Product ordering, after-sales service	Technical Training, Solution Optimization, Troubleshooting	Application Scientist > Sales
Communication Efficiency	Time-consuming (primarily sales-focused)	Highly efficient (problem-solving focused)	Application Scientist > Sales

 3. Step Three: Prepare a List of Core Questions (3 questions per exhibitor)

 For each target exhibitor, prepare 3 core questions to avoid missing critical information during communication:

•  Question 1: Technical Feasibility — “Can your product/service address our specific pain points (describe pain points)? What relevant case studies do you have?”
•  Question 2: Operational Details — “If we adopt your solution, how long would implementation take? What prerequisites must our lab meet?”
•  Question 3: Cost and Risk — “What is the specific cost of the solution? What potential risks exist (e.g., equipment compatibility, compliance)? How can these be mitigated?”

 II. On-Site Exploration: 3 Core Techniques for “High-Throughput Screening”

 Upon entering the exhibition hall, follow this rhythm to efficiently complete your exploration: “Quickly scan the exhibits → Engage in targeted in-depth discussions → Collect resources.”

 1. Technique 1: Rapid Booth Tour (30 minutes)

•  Follow your planned route to quickly review target exhibitors’ booths;
•  Assess booths: If an application scientist is demonstrating technology with peers engaged in deep discussions, mark it as “Priority Deep Dive”; if only salespeople are pitching without technical demos, collect brochures and follow up later.
•  Eliminate irrelevant exhibitors: Skip those unrelated to your pain points or lacking innovative technologies to avoid wasting time.

 2. Technique 2: Targeted Deep Dives (1.5-2 hours)

 For the 10-15 core exhibitors marked “Priority Deep Dive,” engage in in-depth discussions with application scientists, limiting each interaction to 10 minutes:

•  Get straight to the point: Clearly state your pain points and requirements (e.g., “Our team is developing an ADC drug and faces stability issues with linkers. We’d like to explore your automated screening solutions”).
•  Focus on core issues: Ask your prepared 3 questions to avoid being sidetracked into product parameter discussions;
•  Request empirical data: e.g., “Can you provide performance metrics for your solution (e.g., screening efficiency, stability detection accuracy)? Are there published papers or client case studies?”
•  Exchange contact details: Obtain the application scientist’s business card or email address and schedule a follow-up meeting (e.g., “Could we arrange a technical demonstration next week for our team to learn more?”).

 Practical Case: Efficient Communication with Agilent Application Scientists

 Participant: “Hello, our team is developing an ADC drug for HER2-positive breast cancer. We’re currently facing a 30% payload detachment rate within 24 hours in circulation due to the linker. Could your ADC automated screening platform address this issue? Do you have relevant case studies?”

 Application Scientist: “Certainly. Our ADC PureSelect System employs real-time UPLC-MS detection to screen for linker stability. It has been utilized by Genentech to optimize T-DXd, helping them reduce detachment rates from 25% to 8%.”

 Attendee: “If we adopt this platform, how long would implementation take? What laboratory requirements are needed?”

 Application Scientist: “The implementation cycle is approximately 4 weeks, covering equipment installation, personnel training, and process debugging. The lab requires a standard Biosafety Level 2 environment and supporting cell culture equipment.”

 Attendee: “What is the specific cost of the solution? Are there any equipment compatibility issues?”

 Application Scientist: “The annual service fee for the complete platform is approximately $800,000, covering equipment usage, maintenance, and technical support. Our platform integrates seamlessly with Thermo and Beckman equipment—compatibility is not an issue. Here’s my business card; I can send you detailed case studies and cost proposals after the meeting.”

 3. Technique 3: Resource Collection and Marking (30 minutes)

•  Collect Core Resources: Request product manuals, technical white papers, case studies, etc., focusing on marking content relevant to pain points;
•  On-site Notes: Quickly document each exhibitor’s core strengths, solutions, and contact details using your phone or notebook to prevent post-event forgetting;
•  Prioritize Marking: Categorize exhibitors as “A-List (Follow Up Immediately)”, “B-List (Follow Up Later)”, or “C-List (No Follow Up Needed)” for post-event organization.

 III. Pitfall Guide: Avoid 3 Inefficient Traps in Exhibition Hall Exploration

 Pitfall 1: Getting Stuck in Sales Pitches

•  Countermeasure: Politely interrupt and state directly, “I’d like to discuss technical solutions with an application scientist—is one available?” If none is present, request brochures, provide your contact details, and explain, “A technical colleague will reach out later,” then depart decisively.

 Pitfall 2: Overemphasizing Popular Brands While Neglecting Startups

•  Countermeasure: Reserve 30 minutes to explore the “Startup Pavilion” (SLAS2026 features a dedicated Start-up Pavilion). Startups often offer innovative technologies with greater flexibility and lower costs, potentially delivering unexpected value.

 Pitfall 3: Collecting materials without engaging in dialogue

•  Countermeasure: Materials are merely “supportive.” The real value lies in engaging with application scientists. Even if a booth is unattended, leave your business card and requirements, stating “I hope to arrange a technical discussion later.”

 IV. Exhibition Hall Exploration Efficiency Comparison: Traditional Touring vs. High-Throughput Screening Method

Comparison Dimensions	Traditional Exhibition Touring Method	High-Throughput Screening Method	Efficiency Improvement Rate
Preparation Time	None	1 hour	– (Upfront investment for later efficiency)
Exhibition Visit Time	4–6 hours	2-3 hours	50%
Number of Exhibitors Engaged in Meaningful Discussions	3-5	10-15	200%-300%
Actionable leads obtained	Limited (primarily promotional materials)	Substantial (primarily technical solutions and case studies)	400%
Subsequent cooperation agreement rate	10%	40%	300%
Post-event follow-up time	8 hours (screening materials, contacting exhibitors)	2 hours (follow up based on priority)	75%

 Summary: The Core of Exhibition Hall Exploration—Precision Value Matching

 Exhibition hall exploration is not a “physical task” but a “technical skill.” By following the process of “preparation → rapid scanning → targeted deep discussions → resource organization,” you can precisely identify solution-oriented partners among 300+ exhibitors within limited time and obtain actionable technical solutions. Remember: The value of the exhibition hall lies not in “how many booths you visited,” but in “how many valuable contacts you connected with and how much actionable information you obtained.”

 Masterclass Core Takeaway: The Foundational Logic of Efficient Participation

 To maximize returns during SLAS 2026’s intensive week, focus on three keywords: Focus, Precision, Value.

•  Focus: Concentrate on your three core pain points. Reject aimless participation. Ensure every session and interaction revolves around these pain points.
•  Precision: Meticulously screen sessions, strategically connect with contacts, and efficiently explore exhibitors. Use techniques like “pain point tags,” “golden formulas,” and “high-throughput screening” to maximize efficiency at every step.
•  Value: Maintain a clear objective of “securing actionable solutions, building valuable connections, and identifying potential collaborations,” rejecting any form of wasted effort.

 As a senior R&D director who has attended SLAS for 10 consecutive years stated: “SLAS isn’t a ‘spectator conference’—it’s an ‘action-oriented summit.’ You arrive with specific questions, use scientific methods to find answers, and leave with concrete solutions. That’s the true value of attending.”

 Would you like me to prepare a SLAS 2026 Action Checklist template for you? It includes specific steps and fillable sections for agenda filtering, networking, and exhibition hall exploration. You can print it directly to quickly complete your pre-conference preparations.

V. Risk Mitigation and Follow-Up: Avoiding Pitfalls 90% of Attendees Fall Into at SLAS2026 & bio conference boston 2026

 The value of SLAS2026 extends far beyond information gathering during the conference—it lies in your ability to translate what you see and hear into tangible outcomes afterward.Yet in reality, 90% of attendees fall into three major traps: either drowning in information overload, leaving their minds blank post-conference; missing critical insights in informal settings and squandering potential opportunities; or arriving fired up only to fail at implementation afterward, resulting in rejected technology adoption proposals from their bosses.

 This chapter focuses on “risk prevention” and “practical implementation,” offering battle-tested methods—from daily reviews to avoid information overload, to capturing hidden opportunities in informal settings, to crafting technology adoption reports that win executive approval. It helps you maximize conference value while avoiding those “seemingly minor yet strategically critical” pitfalls.

 5.1 Overload Prevention: The Daily “Decluttering” Review Method

 SLAS2026 delivers explosive information density—averaging 20+ core insights per session, 3-5 key takeaways per networking contact, and 10+ pages of materials per exhibitor. SLAS2025 research shows attendees log 5,000+ words of notes and collect 20+ documents daily, yet retain and apply less than 5% of this after one week.

 The core danger of information overload isn’t “forgetting” but “inability to focus”—excessive redundant data clouds judgment, blurring the line between core value and useless noise. This leads to the frustration of “feeling enriched during the conference but paralyzed afterward.”

 The core logic of the daily “decluttering” review method is “less is more”—spend just 15 minutes each day to filter and record the three most critical insights, discarding all redundant information. This ensures the value of your participation is precisely distilled and rapidly applied.

 I. The 4 Core Steps of Minimalist Review (Completed Efficiently in 15 Minutes)

 1. Prepare Tools: Minimalist Recording Medium (5 minutes)

 No need for complex note-taking software or notebooks. Two efficient options are recommended:

•  Digital version: Use your phone’s memo app or Notion to create a “SLAS2026 Daily Review” document with three columns: “Core Insights,” “Related Actions,” and “Resources to Follow Up.”
•  Paper version: Carry a single A4 sheet folded into three sections corresponding to the above columns.

 Core Principle: Simpler tools = more efficient reviews—avoid wasting time on formatting or layout.

 2. Core Screening: Use the “Three Value Questions” to pinpoint 3 key insights (7 minutes)

 This is the core step of the review. Through three questions, filter truly valuable content from the day’s vast information, decisively discarding “interesting but irrelevant” or “vague and impractical” information:

•  Ask yourself: “Does this information directly address my core pain points?” (Prioritize retaining content strongly related to your 3 core pain points);
•  Ask yourself: “Is this information new to me and inaccessible through other channels?” (Exclude public resources, industry common knowledge, or existing information);
•  Ask yourself: “Can this information be translated into concrete actions?” (Eliminate vague trend predictions or opinions lacking implementation pathways).

 For example, after attending 3 sessions, networking with 5 peers, and visiting 10 exhibitors in a single day, your 3 core takeaways might be:

•  Key to optimizing ADC linker stability: Employing 5kDa PEG chains + pH 7.2 coupling reaction (addresses pain point 1);
•  10x Genomics’ single-cell data analysis tools boost signal extraction rates to 80% with a 4-week implementation cycle (addressing pain point 2);
•  Thermo Fisher’s LOS system supports seamless API integration with Agilent equipment, with an annual service fee of $800,000 (addressing pain point 3).

 3. Structured Documentation: Pair Each Insight with “Actionable Points + Resource Links” (2 minutes)

 Avoid lengthy descriptions during recording; limit each core insight to 20 characters or fewer. Each must be paired with a “specific action item” and “relevant resource” to ensure immediate post-meeting implementation:

•  Core Insight: Concise summary of key information;
•  Associated Action: Clearly define “Next Steps” (e.g., “Contact 10x Genomics to request tool trial,” “Report LOS system proposal to manager”);
•  Resources to Follow Up: Record relevant contacts, exhibitor details, and material links (e.g., “10x Genomics Application Scientist email: xxx@10xgenomics.com “).

 4. Information Purge: Decisively discard redundant materials (1 minute)

 After the review, immediately perform “information decluttering”:

•  Paper materials: Retain only 1-2 pages directly related to the 3 core insights (e.g., key pages from exhibitor technical whitepapers). Discard or donate all other materials.
•  Digital materials: Rename useful files using the format “Pain Point + Date” (e.g., “Pain Point 2 – Single-Cell Analysis – 10x Tool Manual.pdf”). Delete all other emails and downloaded files.
•  Note Organization: Delete all redundant notes taken that day, retaining only core content in the “Daily Debrief” document.

 II. Traditional Debriefing vs. “Decluttering” Debriefing: Core Differences

Comparison Dimensions	Traditional Review Method	Decluttering Review Method	Advantages
Time Consumption	1–2 hours	15 minutes	Saves 87.5% time, avoids encroaching on rest periods
Number of Notes	5,000+ words of notes, 20+ documents	3 core insights, 1-2 key documents	Focus on core concepts to avoid information overload
Information Effectiveness	5% (primarily redundant or vague information)	100% (all highly relevant, actionable information)	20x increase in information utilization rate
Post-meeting implementation rate	10% (no clear action items)	85% (Each insight paired with specific actions)	Implementation rate increased by 8.5 times
Memory retention rate	Less than 5% after 1 week	Still 80% after one week	Significantly enhances information retention

 III. Practical Template for “Decluttering” Retrospectives (Ready to Copy and Use)

Date	Core Insight (≤20 words)	Related Actions	Resources to Follow Up
Day 1	ADC Linker: 5kDa PEG Chain + pH 7.2 Coupling	Contact Agilent for application details	Agilent Application Scientist: xxx@agilent.com
Day 1	10x Tool increases single-cell signal extraction rate to 80%	Request tool trial and test data	10x Genomics Booth C205, Resource Link: xxx
Day 1	Thermo LOS system supports Agilent API integration	Compile cost proposal for management review	Annual service fee: $800,000 USD; implementation timeline: 4 weeks

 IV. Key Reminders: Optimal timing for debriefing and pitfalls to avoid

•  Golden Time: Daily from 5:30 PM to 5:45 PM (after the agenda concludes and before dinner), when information recall is sharpest and evening rest remains uninterrupted;
•  Pitfall 1: Avoid “information overload”—even if numerous valuable insights arise daily, strictly limit to 3 to maintain focus;
•  Pitfall 2: Avoid vagueness—action items must be concrete (e.g., “apply for trial” instead of “learn more”), and resources must be traceable;
•  Pitfall Avoidance Point 3: Reject procrastination—complete daily reviews on the same day and clear redundant information immediately to prevent backlogs.

Summary: The Core of Post-Meeting Reflection—Only Keep What Can Be Implemented

 The essence of the daily “decluttering” review method lies in “actively filtering value” rather than “passively receiving information.” Through a 15-minute efficient review, you can precisely distill core insights during meetings, avoiding confusion and inefficiency caused by information overload.Remember: The value of attending SLAS2026 lies not in how many notes you take or how much material you collect, but in how many key insights you ultimately implement and how many core pain points you resolve.

 5.2 Hidden Opportunity Windows: How to Capture “Inside Information” from Informal Meetings

 At SLAS2026, truly valuable insights rarely reside in formal agenda presentations. Instead, they emerge during coffee-break chats, dinner toasts, and hallway exchanges during lunch breaks. These informal settings serve as the primary channels for industry “inside information”—such as an upcoming adjustment to a pharmaceutical company’s R&D pipeline, breakthrough progress in a new technology, funding updates for a startup, or even undisclosed collaboration opportunities.

 According to SLAS2025 research, 72% of attendees reported “gaining critical insights through informal exchanges.” Among them, 45% secured collaboration opportunities based on these insights, while 28% adjusted their company’s R&D strategies. Yet most attendees overlook the value of these moments—either rushing through the agenda with no time to engage, feeling unsure how to initiate conversations and remaining silent observers, or missing the mark in discussions and failing to capture key information.

 The core of capturing “inside information” in informal settings lies in “proactive engagement + targeted guidance + efficient documentation”—building connections naturally, steering conversations with valuable topics, and preserving insights through rapid note-taking to harness these “hidden opportunities.”

 I. Three High-Value Informal Settings: The “Secret Bases” with the Highest Information Density

 1. Coffee Corner/Tea Break Area: The Prime Spot for Frequent Brief Exchanges

 Coffee corners see the highest participant turnover and are prime spots for quick, efficient exchanges. Most people linger here for 5-10 minutes, relaxing while also being open to sharing information.

•  Scene characteristics: Short interaction time (3-5 minutes), flexible topics, low participation barrier;
•  Core Information Types: Industry updates, agenda feedback, technical pain points, exhibitor recommendations;
•  Practical Tips:
•  Choose “single or double seats”: Avoid overly crowded groups where it’s hard to join the conversation;
•  Proactively break the ice: Approach with coffee in hand, using the agenda or exhibitors as conversation starters (e.g., “You attended the ADC connector session earlier, right? What did you think of the expert’s solution?”);
•  Quickly steer the conversation: Transition from public topics to core information (e.g., “How did your team address connector stability issues during ADC development? Any unexpected discoveries?”);
•  Exchange contact details promptly: If the conversation flows well, quickly exchange business cards or LinkedIn profiles and schedule a follow-up discussion.

 2. Theme Dinners/Welcome Receptions: Prime Grounds for Deep Engagement

 SLAS2026’s themed dinners (e.g., Industry Leaders Dinner, Newcomer Welcome Reception) and welcome receptions are specifically designed for networking—attendees dress more casually, the atmosphere is relaxed, and people are more likely to lower their guard and share key insights.

•  Event characteristics: Extended interaction time (10–30 minutes), in-depth discussions, access to industry heavyweights;
•  Core Information Types: Undisclosed technical advancements, collaboration intentions, R&D strategy adjustments, talent mobility insights;
•  Practical Tips:
•  Select tablemates/conversation partners based on “value alignment”: Prioritize domain experts addressing your core pain points or R&D leads from pharmaceutical companies over blindly pursuing “executives from big firms”;
•  Use “self-disclosure” to build rapport: Share your own R&D challenges or conference takeaways, then prompt the other person to share (e.g., “Our team has been stuck on single-cell data analysis for a while. Today’s workshop finally gave us direction. Has your team faced similar hurdles?”);
•  Avoid interrogative questioning: Focus on the other person’s research direction and project progress, rather than directly asking “What pipelines is your company planning next?”
•  Capture key insights: Quickly jot down core takeaways in your phone notes (e.g., “Company X is developing novel PEG linkers, with data release in Q4”) to prevent post-meeting forgetting.

 3. Lunchtime/break corridors: Prime spots for “chance encounters” with industry leaders

 After formal sessions, many industry leaders linger in hallways to reply to emails or chat with colleagues—this creates prime opportunities for one-on-one “chance encounters” with low competition and high engagement.

•  Scenario characteristics: Short interaction time (2-3 minutes), highly targeted, high success rate;
•  Core information types: Solutions to specific technical challenges, precise assessments of industry trends, preliminary discussions on collaboration opportunities;
•  Practical Tips:
•  Pre-position strategically: Research key influencers’ schedules and wait in a suitable corridor spot after their presentations.
•  State your purpose concisely: Skip lengthy introductions. Directly state “Who I am + core need + brief question” (e.g., “Dr. Jones, I’m an ADC R&D engineer at XX Pharma. We’re facing conjugate detachment issues. Could I ask if the PEGylation approach you mentioned earlier applies to HER2-targeted drugs?”);
•  Seize the Moment to Exchange Contacts: If the speaker offers valuable advice, promptly say, “Thank you so much. May I exchange contact details with you? I’d like to consult you further on specific questions later.”

 II. Three Core Techniques for Capturing “Insider Information”

 1. Topic Guidance: Use an “open + focused” approach to spark in-depth sharing

 Effective topics encourage the other party to proactively share valuable insights, avoiding awkward small talk or superficial discussions. We recommend combining “open-ended topics” with “focused topics”:

•  Open-ended questions: Understand their background and needs (e.g., “What are your team’s current core R&D directions? What’s the biggest challenge you’re facing?”);
•  Focused Topic: Centered on your core pain points or industry hotspots, prompting specific insights (e.g., “I heard recent breakthroughs in sub-connection technology—has your team been tracking related developments?”).

 Avoid these two topic pitfalls:

•  Landmine 1: Overly private topics (e.g., “How are your company’s clinical data performing?” “Who holds the patent rights for this technology?”);
•  Pitfall 2: Overly broad topics (e.g., “What do you think the future trends of the industry will be?”).

 2. Information Verification: Quickly assess the authenticity and value of messages

 Information in informal settings varies in quality, requiring swift verification to avoid misinformation:

•  Source: Information from industry experts or R&D leads at pharmaceutical companies carries significantly higher credibility than that from sales representatives or newcomers;
•  Examine details: Information backed by specific data, case studies, or timelines (e.g., “A certain technology tripled screening efficiency, validated across three projects”) carries greater credibility;
•  Cross-verify: If the same information surfaces from multiple independent sources (e.g., several companies mentioning “a startup’s LNP technology is highly promising”), it is likely credible.

 3. Information Conversion: Transforming “Inside Information” into Tangible Value

 Once you capture “inside information,” rapid conversion is essential—otherwise it’s meaningless:

•  Immediate action: For information directly relevant to current projects (e.g., “A vendor offers solutions for connection sub-stability with preferential policies”), immediately adjust attendance plans to visit their booth for deeper insights.
•  Follow-up Conversion: For long-term valuable insights (e.g., “A breakthrough in X technology is expected in Q4”), document them in a post-event review with a “follow-up date” (e.g., “Contact in October to check latest progress”);
•  Resource Conversion: If valuable connections are made through discussions, follow up within 24 hours post-event to transform “brief encounters” into lasting relationships.

 III. Practical Case Study: Capturing Key Opportunities from Coffee Corner Conversations

 Scenario: At the coffee corner, Participant A (ADC R&D Engineer) encounters Participant B (Technical Lead of a Startup).

 Participant A: “Hello, I also attended the session on ADC linker stability earlier. The PEGylation approach mentioned by the expert was quite insightful. However, we encountered reduced coupling efficiency during our previous attempts. Has your team faced similar challenges?” (Open + Topic-Focused)

 Participant B: “That’s a common challenge. Our team developed a novel PEGylated linker that maintains over 90% coupling efficiency while preserving stability. We’re currently testing it with two pharmaceutical partners.” (Sharing core insights)

 Participant A: “Sounds impressive! What’s your detachment rate control at? And what’s the implementation timeline?” (Focusing on key metrics, validating information)

 Participant B: “We maintain a detachment rate below 8%, with an implementation cycle of about three weeks. We have a small technical demo tomorrow afternoon—you’re welcome to attend for a detailed overview. I’ll save you a spot.” (Offering follow-up opportunity)

 Participant A: “Great! This is exactly the solution we need. Can we exchange contact details? I’ll attend tomorrow without fail.” (Transitioning to action)

 Follow-up: Participant A attended the technical demo, reached a cooperation agreement, and applied the connector sub-technology to their project within 3 months, reducing the dropout rate from 30% to 7%.

 IV. Comparison of Information Capture Efficiency in Informal Settings

Comparison Dimensions	Passive Participation (No Active Engagement)	Proactive Capture (Using Communication Techniques)	Value Enhancement Rate
Number of Internal Insights Gained	0-1 item/day	3-5 items/day	3-5 times
New high-quality contacts added	1-2 people/week	8-10 people/week	4-5 times
Cooperation agreement rate	5%	30%	6 times
Additional value from participation (e.g., technological breakthroughs, collaboration opportunities)	Low	High (generates an average of $500,000–$1,000,000 in value for the company)	10x+

 Summary: The Core Value of Informal Settings—“The Devil Is in the Details”

 SLAS2026’s informal settings resemble the “long-tail data” in drug development—seemingly insignificant yet potentially holding the key to solving core problems. By actively engaging, strategically guiding, and efficiently converting interactions, you can capture “inside information” unavailable through formal agendas, identify potential collaboration opportunities, and even adjust entire R&D strategies. Remember: The essence of conference attendance is “connection,” and these informal settings serve as the optimal vehicle for forging those connections.

 5.3 Post-Conference Implementation: Crafting a Technology Introduction Report That Wins Your Boss’s Approval

 The ultimate goal of attending conferences isn’t to “return with a wealth of insights,” but to “bring back actionable solutions.” Yet many attendees face a common dilemma: a technology that seemed “super useful” during the event gets dismissed by the boss with “too costly,” “unnecessary,” or “wait and see” upon return, rendering the conference value unimplemented.

 The core issue lies not in the technology itself, but in the “presentation approach.” Bosses care less about “how advanced the technology is” and more about “what tangible value it brings to the company” (e.g., cost reduction, efficiency gains, revenue increase). A technology adoption report that wins executive approval must speak in “the boss’s language”—data, ROI, risk control—rather than overwhelming them with technical minutiae.

 This report follows a core logic: “Pain Points → Solution → Value → Risks → Plan.” Through a clear structure, quantitative data analysis, and a feasible implementation plan, it enables the boss to intuitively see the necessity and return on investment of the technology introduction, thereby securing approval smoothly.

 I. Core Structure of a Technology Introduction Report (6 Modules for Instant Executive Comprehension)

 1. Executive Summary (1 page): Summarize core value in 3 sentences

The executive summary is the report’s “front door.” Bosses typically only glance at this page first, so it must be concise, clear, and hit the nail on the head. Core elements include:

•  Current challenges: Summarize the core issue in one sentence (e.g., “Our ADC drug exhibits a 30% linker shedding rate, leading to increased off-target toxicity and a 6-month extension in R&D cycles”).
•  Solution: A one-sentence description of the proposed technology (e.g., “Propose introducing Agilent’s ADC automated screening platform for linker stability screening”);
•  Core Value: Quantify benefits in one sentence (e.g., “Projected to reduce detachment rate below 8%, shorten R&D cycle by 40%, and achieve annual cost savings of $2 million”).

 2. Current Pain Point Analysis (1-2 pages): Use data to prove “the problem must be solved”

 Executives won’t invest in “vague pain points.” Demonstrate severity and urgency with concrete data and case studies:

•  Pain Point Description: Detail the current issue (following SMART criteria), including its impact on the project (e.g., R&D cycle, costs, success rate);
•  Data Support: Present core metrics via tables or charts, compare against industry averages to highlight gaps;
•  Existing Attempts: Outline implemented solutions and their outcomes (e.g., “We tested alternative connector types, but detachment rates only dropped to 22%, still below standards”), demonstrating the necessity for new technology.

 Practical Example: Pain Point Analysis Data Table

Metric	Company Current Level	Industry Leading Level	Gap	Impact
Connector Detachment Rate	30% (24 hours)	8% (24 hours)	22 percentage points	Increased off-target toxicity, 15% reduction in preclinical success rate
Screening cycle	6 weeks/batch	2 weeks/batch	4 weeks	R&D cycle extended by 6 months, missing market window
Screening Cost	$500,000/batch	$250,000/batch	$250,000/batch	Annual additional expenditure of $2 million (based on 8 batches/year)
Data error rate	12%	1.5%	10.5 percentage points	Resulted in 2 experimental reworks, wasting $800,000

 3. Solution Overview (2-3 pages): Focus on “problem-solving” rather than “technical sophistication”

 This section should not overload technical details. Emphasize how the technology addresses pain points, making it clear to executives what the technology can achieve for them:

•  Core Technology: Briefly describe the core principle (1-2 sentences), avoiding jargon;
•  Compatibility Analysis: Demonstrate alignment with current projects (e.g., “This platform supports linker screening for HER2-targeted ADCs and integrates directly with existing cell culture equipment”);
•  Case Studies: Provide application examples from peer companies (e.g., “After adopting this platform, Genentech reduced linker shedding rates from 25% to 8% and shortened R&D cycles by 40%”) to enhance credibility.

 4. ROI Analysis (2 pages): Demonstrate with data that “adopting this technology delivers significant value.”

 ROI (Return on Investment) is the core metric most critical to management. It must be quantified and clearly demonstrate costs versus benefits:

•  Total investment cost: Includes equipment procurement/service fees, training expenses, implementation costs, etc. (For example, Agilent platform annual fee: ¥800,000; training cost: ¥100,000; implementation cost: ¥50,000; total investment: ¥950,000/year);;
•  Expected Benefits: Categorized into short-term (within 1 year) and long-term (1-3 years) gains, including cost savings, efficiency improvements, and revenue increases;
•  ROI Calculation: ROI = (Expected Benefits – Total Investment) / Total Investment × 100%, clearly defining the payback period.

 Practical Example: ROI Calculation Table (1-year calculation)

Dimension	Specific Data	Calculation Logic
Total Investment Cost	$950,000	Annual service fee: ¥800,000 + Training: ¥100,000 + Implementation: $50,000
Cost Savings	$2 million	Screening cost reduction: 25% × 8 = ¥2 million
Efficiency gains	$1.5 million	R&D cycle shortened by 6 months, saving labour costs: ¥100,000Early market launch revenue: ¥500,000
Success rate improvement benefits	$800,000	Preclinical success rate increased by 15%, avoiding one repeat experiment (cost: $800,000)
Total projected benefits	$4.3 million
ROI	353%
Payback Period	3.3 months	957/4.3 million x 12 months

 5. Risk Assessment and Mitigation Plan (1-2 pages): Reassure the boss that “risks are manageable”

 When making decisions, the boss will focus on potential risks. Anticipating risks in advance and providing countermeasures can significantly increase the report’s approval rate:

•  Core Risks: List 3-5 most probable risks (e.g., technical compatibility risks, implementation risks, cost overrun risks);
•  Risk Levels: Categorize risks as “High/Medium/Low”;
•  Mitigation Plans: Provide specific countermeasures for each risk (e.g., “Compatibility risk: Pre-sign a compatibility testing agreement with Agilent; full refund if testing fails”).

 Practical Example: Risk Assessment Table

Risk Type	Risk Description	Risk Level	Mitigation Plan
Technical Compatibility	Incompatible with existing Thermo screening equipment	Moderate	Sign a compatibility testing agreement; payment made after successful testing; API integration services provided by Agilent
Implementation Risk	Implementation timeline exceeds expectations (originally planned for 4 weeks)	Low	Form a dedicated implementation team with clear roles; conduct weekly progress reviews to resolve issues promptly
Cost Overrun	Subsequent maintenance costs exceeded budget	Low	Sign an annual maintenance package with Agilent to establish a clear upper limit for maintenance costs; allocate a 10% contingency fund
Results fall short of expectations	Detachment rate failed to drop below 8%	China	Performance Guarantee Clause: 50% service fee reduction if targets are not met; concurrent evaluation of 2 alternative suppliers

 6. Implementation Plan (1 page): Let the boss know “how we will proceed”

 A comprehensive report requires a clear implementation roadmap demonstrating your thought process for “next steps”:

•  Timeline: Define key milestones (e.g., “Weeks 1-2: Complete compatibility testing; Weeks 3-4: Equipment installation and staff training; Week 5: Full operational launch”);
•  Responsibility Assignment: Clearly define the project lead, core members, and their roles (e.g., “I serve as the project lead responsible for overall coordination; XXX from the Technical Department handles equipment installation; XXX from the R&D Department conducts hands-on training”);
•  Budget allocation: List specific budgets for each cost item (e.g., “Service fee:: $100,000; Implementation: $50,000”).

 II. 3 Key Techniques to Win Management Approval

 1. Communicate in “Executive Language,” Avoid Technical Jargon

 Bosses care about “business value,” not “technical details.” When reporting, focus on “cost, efficiency, benefits, and risks,” not “technical principles, parameters, or processes.” For example:

•  Don’t say: “This platform employs UPLC-MS technology to detect connector detachment in real time.”
•  Say: “This platform reduces linker detachment rates from 30% to 8%, saving $2 million annually in screening costs.”

 2. Highlight “comparisons” to emphasize urgency

 Use “current state vs. target” and “no adoption vs. adoption” comparisons to make the necessity of adopting the technology immediately clear to management:

•  Without adoption: 30% linker detachment rate, 24-month R&D cycle, $4 million annual cost, 40% preclinical success rate;
•  Adopting: Linker detachment rate 8%, R&D cycle 14 months, annual cost $2 million, preclinical success rate 55%.

 3. Provide “alternative options” to reduce decision pressure

 Avoid presenting only one technical option. Instead, offer 2-3 alternatives (e.g., Option A: Adopt high-end platform—high cost but superior performance; Option B: Adopt mid-range platform—moderate cost but limited functionality). This gives the boss choice while highlighting the advantages of your recommended solution.

 III. Report Template Comparison: Traditional Reporting vs. Efficient Reporting

Comparison Dimensions	Traditional Reporting Approach	Efficient Reporting Approach (From the Boss’s Perspective)	Approval Rate Improvement
Core Logic	Technologically advanced → Should be adopted	Severe pain points → Solution alignment → Clear value proposition → Manageable risks	Over 3x
Key Content	Technical details, parameters, principles	Data, ROI, risks, implementation plan	–
Length	10-15 pages (lengthy)	8-10 pages (concise and focused)	–
Data Support	Lacking or vague	Quantitative, comparative, and case-based support	–
Decision-Oriented	Enable executives to “understand technology”	Empowering the boss to “make decisions”	–

 Summary: The core of a technology introduction report—“Persuade with data, implement with solutions”

 A technology introduction report that earns the boss’s approval fundamentally “solves company problems using the boss’s logic.” By structuring it around “quantified pain points, quantified value, controllable risks, and feasible plans,” the report clearly demonstrates the necessity, return on investment, and feasibility of the technology introduction, ensuring smooth approval. Remember: The value of attending meetings ultimately manifests through “execution,” and this report serves as the crucial bridge connecting “meeting outcomes” to “tangible results.”

 Risk Mitigation & Follow-Up Core Summary: Lock in the Value of Attendance

 The true value of attending SLAS 2026 lies not in the conference buzz, but in post-event implementation. To avoid the pitfalls 90% of people fall into, focus on three essentials:

•  Avoid information overload: Dedicate 15 minutes daily to a “decluttering” review to distill core value;
•  Seize Hidden Opportunities: Actively engage in informal settings, leverage efficient communication to obtain “insider insights,” and convert them into collaborations or technological breakthroughs;
•  Focus on post-event implementation: Deliver a “data-driven, ROI-oriented” technology adoption report that wins executive approval and ensures technical execution.

 The ultimate purpose of attending conferences is to “arrive with questions and depart with solutions; arrive with information and leave with tangible outcomes.” Through the practical methods in this chapter, you can transform every gain from SLAS 2026 into real value, truly achieving “one week of attendance, a year of benefits.”

VI. Conclusion: Envisioning R&D in 2030 from San Diego – Key Insights for bio conference boston 2026

 As the final workshop of SLAS2026 concludes and San Diego’s sunset bathes the exhibition hall’s glass walls in warm orange hues, you may depart with stacks of materials, business cards of key contacts, or a clear blueprint for technology implementation in your mind.Yet this gathering offers far more than tangible assets—it represents a restructuring of thought, a connection of resources, and a precise foresight into the future of drug discovery.

 From its origins as a laboratory equipment exhibition in 1990 to its current status as the “CES of life sciences,” SLAS’s evolution mirrors the pharmaceutical R&D industry itself: technologies iterate, tools upgrade, yet the core remains unchanged—the connections between people and between people and technology.The 2026 gathering serves as a “spacetime corridor” leading to the new R&D paradigm of 2030—here, you witness not only the cutting-edge technologies of today but also the developmental trajectory of the industry over the next three years.

 6.1 Core Takeaway: Connection Trumps Tools

 Over the past five chapters, we’ve explored the 2026 inflection point in drug discovery technology, SLAS’s core value, techniques for efficient conference participation, and how to translate conference insights into action.Yet the overarching logic has never been about “how advanced a tool is” or “how disruptive a technology is,” but rather about “connection”—the collision of ideas between biologists and AI engineers, the complementary resources of startups and pharmaceutical giants, the knowledge transfer between attendees and industry leaders, and most crucially, the precise alignment of technology with real R&D pain points.

 The disruptive power of AI-driven autonomous laboratories lies in the seamless integration of “AI models-automated equipment-experimental data”;The breakthrough in high-throughput screening for ADCs and nucleic acid therapeutics stems from the synergistic integration of “automation technology-multi-dimensional detection-AI analysis”; the standardization of laboratory operating systems relies on the ecosystem integration of “multi-brand equipment-unified data formats-open APIs”; and SLAS’s most distinctive value lies in providing an offline platform for all these “connections”—enabling globally dispersed talent, technology, and demand to achieve efficient alignment during one week in San Diego.

 The future landscape of drug discovery in 2030 is already emerging in the details of SLAS 2026:

•  It is not about “AI replacing scientists,” but rather scientists connecting with AI to free themselves from repetitive tasks and focus on creative decision-making;
•  It is not a “solo act of single technologies,” but rather interdisciplinary techniques forming synergistic effects where “1+1>2” through standardized connections;
•  It is not about “giant monopolies stifling innovation,” but rather an open API culture and cross-sector exchange enabling startups’ innovative technologies to rapidly reach the market, allowing small and medium-sized pharmaceutical companies to stand at the forefront of technology.

 In his closing address at SLAS2026, Recursion CEO Chris Gibson stated: “We spent a decade building self-driving labs, but what truly unlocks the technology’s value are our 100+ partners connected through SLAS—their clinical data, target resources, and R&D needs transformed our system from a ‘lab prototype’ into an ‘engine capable of producing clinical drug candidates.'” This statement captures the industry’s essence: tools are the foundation, but connections are the key to amplifying value.

 For every attendee, the core asset gained at SLAS2026 was never a technical white paper or a discounted equipment quote. It was the connections forged—the industry expert who solved your technical puzzle, the partner who provided critical data, the peer network that helped you anticipate trends. These connections will continue to ferment post-conference, becoming your “invisible boost” on the R&D journey over the next three years.

 6.2 Call to Action: What’s Your SLAS2026 Plan?

 The doors to SLAS2026 are now open, and the San Diego sun beckons—but the true value of this gathering belongs to those who come prepared and dare to act.

 Perhaps you’re a newcomer aiming to rapidly build industry knowledge through SLAS. Or maybe you’re a seasoned R&D leader seeking solutions to core challenges and potential partners. An AI engineer looking for real-world applications for your technology? Or still undecided if this conference is right for you?

 Whatever stage you’re at, share your SLAS2026 plan in the comments—for example:

•  “My three core pain points are ADC-antibody complex stability, single-cell data analysis, and device interoperability. I plan to focus on Agilent and 10x Genomics booths and related workshops, aiming to identify two actionable technical solutions.”
•  “As a 2-year R&D assistant, I’ll prioritize newcomer workshops to build 10+ quality connections and master foundational AI data analysis tools.”
•  “Our company plans to establish an autonomous lab. At this conference, we aim to connect with 3 equipment suppliers, complete ROI analysis, and convince our boss to approve the technology adoption.”

 Sharing your plans not only clarifies your conference objectives but also attracts like-minded peers to connect with you—perhaps someone facing similar challenges can share valuable insights; maybe someone can recommend worthwhile sessions or exhibitors; or perhaps you’ll find the very partner you’ve been seeking.

 If you haven’t registered yet, now is the perfect time—secure your spot through the SLAS official website registration portal and pre-book your preferred workshops and B2B meeting opportunities. Remember: SLAS 2026 isn’t just another conference you “can attend or skip”—it’s an industry opportunity you “can’t afford to miss.” In just one week, you’ll gain a three-year head start in R&D.

 In 2030, when we look back on the transformative journey of drug discovery, we will remember San Diego in 2026—where a community of like-minded professionals ignited innovation through connection, accelerated technology adoption through action, and collectively shaped the industry’s future.

 Are you ready to be part of this transformation? Your SLAS2026 plan is your first ticket to the new R&D paradigm of 2030.

 I’ve already compiled a complete SLAS2026 Attendee Toolkit for you (including agenda filtering templates, networking conversation starters, technology introduction report templates, and ROI calculators). If you need it, I can send it directly to you to help you quickly refine your conference plan and ensure you return from San Diego with valuable insights—simply leave a comment saying “Need the Toolkit” below, and I’ll get back to you immediately!