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This track will highlight recent innovations in assay development and screening, spanning novel technologies, creative assay design, and impactful screening strategies. Presentations will focus on approaches aligned with next-generation therapeutic modalities and emerging mechanisms of action, addressing the challenges of more complex and less tractable targets. The role of AI in transforming assay development will be explored, including the move toward prediction-first and data-driven design. Through practical case studies, speakers will showcase state-of-the-art cellular, biochemical, and biophysical assay platforms deployed in model systems of increasing biological complexity.

Advanced Imaging & Phenotypic Screening
Session Chair: Samantha Peel, PhD (AstraZeneca)
This session will explore novel approaches to characterising and exploiting subcellular/organelle-level phenotypes to drive mechanistic and therapeutic research.

Novel Assays in Complex In Vitro Cell Models
Session Chair: Tim Spicer, PhD (UF Scripps)
This session will focus on scaleable, novel read-outs from patient centric, complex in vitro cell models, highlight cutting-edge innovations in assay development leveraging complex, patient-relevant in vitro models to enable next-generation drug discovery. This includes scalable, high-content, and novel functional readouts derived from advanced cellular systems, including iPSC-derived models, organoids, co-culture systems, and 3D microphysiological platforms. All applications in drug discovery are welcome, particularly the use of patient-derived models to dissect disease biology and support translational discovery, including neuroscience and oncology. Topics of interest include assay strategies for interrogating complex phenotypes, integration of multimodal data, and approaches that balance biological relevance with throughput and robustness. Case studies demonstrating how these models inform target validation, compound profiling, and mechanism-of-action studies are encouraged. This session aims to bring together academic innovators and industry scientists to advance scalable, predictive assay platforms that better capture human disease complexity.

AI for Assay Development and Prediction
Session Chair: Melissa Crisp, PhD (Eli Lilly & Company)
Advances in machine learning and AI are transforming Drug Discovery. This session explores how AI is being leveraged across the discovery pipeline, from project design and assay development to the prediction of cellular responses to genetic and chemical perturbations. Speakers will share how their organizations are applying AI driven approaches, from autonomous experimental design to optimization strategies, to accelerate assay development and generate higher quality biological insight.

Emerging Assays and Screening Technology
Session Chair: Joseph Shaw, PhD (AstraZeneca)
Topics will include emerging, scaleable assay technology and screening methods to tackle the most challenging of readouts. Complete description coming soon.

Advances in Assays for Induced Proximity
Session Chair: Agnieszka Konopacka, PhD (ICR)
This session explores advances in scalable assay technology for studying Induced Proximity. Topics will include unlocking the undruggable with MGs, RIPTACS and TCIPs.

Advances in Chemical Biology for Mechanistic Insights
Session Chair: Alison Axtman, PhD (University of North Carolina)
This session highlights diverse chemical biology technologies for discovering and validating small molecule modulators across major target classes. Together, these approaches demonstrate how complementary screening, profiling, and mechanistic technologies can advance target validation and reveal cellular mechanisms of action

Automated process workflows underpin chemical and biological research, enabling both small and large molecule discovery and broader scientific advances. This track will share updates from the past year since SLAS2026 and explore emerging opportunities to improve productivity, data quality, and automation reliability. As automation expands further into laboratory execution, experimental design, and data analysis, sessions will examine the evolving role of scientists and technologists, including career pathways for automation and technology professionals. Real-world case studies will highlight successful implementations, challenges encountered, and practical lessons learned.

People at the Heart of Lab Automation: Building Skills, Careers, and Adoption
Session Chair: Lorna Suckling, PhD (GSK)
Investing in automation is only the first step—real value comes from adoption and implementation. This session will share practical insights on how to maximise ROI by focusing on people: hiring for automation capabilities, supporting skill development, and embedding change management. We will also explore how to build and sustain career paths for automation specialists in environments where traditional scientific progression models may no longer apply.

ADMET Automation Success and Innovation
Session Chair: Brandon Santiago, PhD (GSK)
Attendees will gain insights into how emerging analytical technologies, intelligent automation, and end-to-end laboratory orchestration can accelerate ADMET workflows, reduce manual intervention, and enable faster, more informed decisions in drug discovery. The session will emphasize both technological innovation and practical implementation strategies for building the next generation of automated DMPK laboratories.

Automation Technology to Maximize Organic Chemistry Synthesis
Session Chair: Huy Nguyen, PhD (Genentech)
Novel organic compound synthesis is the longest turnaround time in the lead optimization cycle in drug discovery. From intelligent reaction planning to high-throughput experimentation, this session showcases emerging technologies that are advancing autonomous synthesis and expanding the possibilities of modern organic chemistry. Speakers will highlight how AI-driven experimental design, automated synthesis, and iterative reaction optimization can improve efficiency, reproducibility, and scientific discovery.

Automation Technology to Maximize Lead Optimization of Antibodies, Biologics and Synthetic Peptides
Session Chair: Zoe Hughes-Thomas, PhD (GSK)
Discover how advanced automation technologies are accelerating lead optimization for antibodies, biologics, and synthetic peptides. This session highlights GSK's closed-loop antibody discovery automation, AstraZeneca's development of a laboratory of the future in Kendall Square, and BigHat Bioscience's lean automation strategies for rapidly transitioning assays from development to production. Speakers will also showcase Broad Institute's automated high-throughput ribosome display platform enabled by variable-cycle qPCR.

AI Enabled Data Flow Coupled to Experimental Path for Target Candidate Profile
Session Chair: Nicolas Houvenaghel, MSc (AstraZeneca)
Artificial intelligence (AI), machine learning (ML), laboratory automation, and experimental science are being integrated to accelerate drug discovery from target identification through lead optimization and candidate nomination. This session highlights approaches to connecting computational predictions with experimental validation, creating iterative, data-driven discovery workflows, from traditional, sequential drug discovery toward closed-loop DMTA systems.

Enabling the Connection Between Automation Processes/Platforms for Target Candidate Profile
Session Chair: Reed Kelso, PhD
Discover how connecting laboratory automation processes, integration software and platforms is transforming drug discovery. This session includes STELLA, an automated flow cytometry workcell designed to improve reproducibility, efficiency, and experimental throughput, alongside practical experiences and lessons learned from automating DMTL workflows. Speakers will explore how integrating laboratory instrumentation, robotic systems, workflow orchestration, and data management can reduce manual intervention and streamline complex experimental processes. Real examples of successfully integrating automated laboratory workcells through orchestration software and robotic transport systems, highlighting implementation challenges, lessons learned, and instances where integration did not go as planned.

Attendees will learn from industry and academic leaders driving the next generation of cellular technologies for drug discovery, translational research, and precision medicine. This track spotlights how patient-derived cell models and New Approach Methodologies (NAMs) — including organoids, spheroids, co-cultures, and microphysiological systems — are providing more human-relevant, translationally predictive alternatives to traditional preclinical models across oncology, neuroscience, metabolic disease, and beyond.

Sessions will explore how these platforms are applied across the discovery and development process: from PK/PD and mechanism-of-action studies, to perturbation screens that move beyond classical dose-response and connect drug effects to cellular states and tissue-level phenotypes. Attendees will also gain insight into the rapidly maturing field of Functional Precision Medicine, where ex vivo drug response platforms built on patient-derived cells are being translated toward real-world clinical decision-making and patient-tailored therapy.

Whether you are developing novel preclinical models, designing perturbation screens to uncover mechanisms of response and resistance, or building functional assays to guide individual treatment decisions, this track offers practical frameworks, emerging data, and the cross-disciplinary connections needed to advance human-relevant biology from bench to bedside.

New Approach Methodologies (NAMs) - Innovations in Preclinical Models for Drug Discovery and Research
Session Chair: John Hickey, PhD (Duke University)
New Approach Methodologies (NAMs) are transforming preclinical research by providing more human-relevant approaches to studying disease, evaluating therapeutics, and predicting drug responses. This session will highlight innovative in vitro experimental models and in silico computational approaches being developed across academia and industry to improve drug discovery and preclinical testing. Speakers will explore applications of these technologies in disease modeling, therapeutic screening, and pharmacokinetic/pharmacodynamic (PK/PD) studies, with an emphasis on improving biological understanding and translational predictivity. Together, these advances offer opportunities to complement or replace traditional preclinical models, accelerate drug development, and improve the translation of discoveries into effective human therapies.

Beyond Dose Response - Perturbation Screens to Uncover Drug Effects in Patient-derived Cells
Session Chair: Takamasa Kudo, PhD (Genentech)
Patient-derived models have become essential tools for functional precision medicine, drug discovery, and modeling human disease biology in vitro. Yet most screens still reduce drug effects to a single viability readout. This application-focused session explores perturbation approaches that go beyond dose-response, linking drug and genetic perturbations to cellular states, phenotypic landscapes, and interactions with the tumor microenvironment. As in vitro models grow more complex and physiologically relevant, technologies such as high-content imaging, single-cell profiling, and Perturb-seq now make it possible to resolve these effects mechanistically, revealing drivers of response and resistance and uncovering new biology.

Patient-derived Cell Models for Functional Precision Medicine
Session Chair: Keith Ligon, MD, PhD (Dana-Farber Cancer Institute)
Functional Precision Medicine (FPM) using patient-derived cell models aims to predict an in vivo patient response from a phenotypic ex vivo assay. Advances in models such as organoids, together with imaging technologies ranging from label-free longitudinal imaging to Cell Painting, are making these assays faster, richer, and more predictive. This session showcases FPM applications on the path to the clinic, including concordance with patient outcomes and turnaround times that fit treatment decisions. With FDA and NIH now actively supporting human-relevant new approach methodologies (NAMs), the field has a real opportunity to define how these assays inform preclinical and clinical decision-making.

The life sciences industry has officially moved past exploratory pilots and entered a period of industrial-scale integration and operational maturity. Making meaningful progress in 2027 demands moving beyond mere prediction toward the implementation of prescriptive, mechanistic, and causal-aware systems capable of navigating astronomical biological and chemical complexity with minimal human intervention.

This track brings together technical experts to address the "generalization gap" in biological and chemical AI and facilitate the integration of computational power with biological reality. We will delve into critical technical frontiers encompassing mechanistic "gray-box" models, the deployment of multi-scale digital twins, and the realization of self-driving laboratories orchestrated by Large Action Models. Join fellow scientists and engineers for technically grounded discussions on scaling autonomous discovery, ensuring biological plausibility, and navigating the new regulatory landscape established by the FDA and EMA.

Mechanistic and Causal AI: From Predictive Patterns to Prescriptive Discovery
Session Chair: Peter McLean, PhD (Recursion)
This session addresses the transition from correlative predictions to prescriptive "gray-box" modeling, embedding mechanistic guardrails from systems biology and physical chemistry into ML frameworks. By integrating physics-based constraints and prior domain knowledge, researchers can ensure both biological and chemical plausibility. We will explore methodologies that prioritize causal inference—enabling the identification of specific molecular interactions and structure-activity relationships—to overcome the generalization gap and meet rigorous demands for transparent, explainable AI in discovery.

Validating the Virtual: In Silico Technologies, Digital Twins and AI Auditors
Session Chair: Alex Frangi, PhD (UK-CERSEI)
This session examines the frameworks required to establish trust in computational modelling and artificial intelligence across the pharmaceutical lifecycle. As the industry operationalises in silico technologies and digital twins, the focus must shift from technical capability to regulatory credibility and verifiable evidence. Presentations will evaluate the deployment of mechanistic, data-driven, and hybrid process models, emphasising the rigorous verification, validation, and uncertainty quantification (VVUQ) standards necessary for regulatory decision-making. To navigate these requirements, we will highlight the UK CEiRSI regulatory airlock mechanism for co-producing best practices between industry and regulators. Furthermore, the session addresses the specific evidence requirements for AI-assisted laboratory auditing. Moving beyond the acceleration of document reviews, we will detail how data architectures must be fundamentally redesigned to ensure continuous, source-traceable evidence that both quality professionals and machine models can reliably inspect. Attendees will leave with practical strategies for embedding robust, regulator-aligned credibility into virtual models and automated quality frameworks.

Autonomous Laboratory Ecosystems: Orchestrating the AI-Native DMTA Cycle
Session Chair: Paul Jensen, PhD (University of Michigan)
This session focuses on the realization of interoperable, globally networked, and hardware-agnostic self-driving laboratories. We will explore the integration of Large Action Models capable of executing multi-step tasks across fragmented systems and adjusting parameters in real-time without human intervention. Join us to discuss API-first hardware, multi-agent architectures, and the scalable future of closed-loop molecular discovery.

Micro- and nanotechnologies have become ubiquitous in biological and medical research because of their ability to control and interrogate environments at the scales of individual molecules, single cells, and more complex biological models (e.g., organoids). They have enabled new measurement modalities and innovative platforms that deliver greater precision, higher throughput, and previously inaccessible data, driving exciting discoveries across the life sciences. Although the adoption of these technologies in commercial workflows continues to grow, integrating them into established systems remains challenging. This track showcases both breakthrough discoveries from fundamental research and emerging micro- and nanotechnologies with the potential to advance lab automation and screening, as well as demonstrated nano/microtechnologies making the leap from research to real-world application. Together, these examples illuminate the path from research innovation to commercial impact.

From Fundamentals to Future Technologies: Micro- and Nano Technologies for Lab Automation
Session Chair: Lior Sepunaru, PhD (UCSB)
This session aims to bridge early-stage micro- and nanofluidic innovation with industry adoption, focusing on breakthrough ideas that can be easily adopted into integrated platforms and real-time adaptive control to reduce cost, improve throughput, and unlock new diagnostic and automation capabilities in the future.

Manipulating at the Microscale
Session Chair: David Huber, PhD (Injectsense)
Cell sorting, droplet screening, microinjection, patch-clamping, and bioprinting are just a small selection of techniques that require the capability to manipulate cells or fluid at the microscale. In this session, we focus on technologies for microscale manipulation with an emphasis on those making the transition to wider adoption and commercial application.

AI-Driven Micro-Systems for Next-Gen Automation
Session Chair: Rajiv Bharadwaj, PhD (10x Genomics)
The convergence of artificial intelligence and miniaturized systems is redefining what’s possible in laboratory automation and lab-in-the-loop paradigm. This session brings together pioneers at the intersection of AI, microfluidics, robotics, and lab-on-a-chip technologies to explore how intelligent micro-systems are building the foundations for next-gen automation.

Developing therapies for debilitating diseases with undruggable or intractable targets requires moving beyond traditional occupancy-driven pharmacology. This track highlights event-based strategies such as targeted protein degradation, alongside advances in AI/ML-enabled drug discovery and emerging modalities including RNA-based therapeutics and antibody–drug conjugates, to enhance efficacy and therapeutic index.

Expanding Induced Proximity Paradigms Beyond Traditional Targeted Protein Segradation (TPD)
Session Chair: Matthew Calabrese, PhD (Pfizer)
Induced-proximity approaches are moving beyond established practice to encompass broader options to control biomolecular function. This session will highlight emerging concepts, technologies, and therapeutic applications across chemical biology and drug discovery, including molecular glues, bifunctional molecules, and proximity-based strategies that reprogram cellular processes and complexes.

Harnessing Novel E3 Ligase Biology to Broaden the Target Space
Session Chair: Amarnath Natarajan, PhD (University of Cincinnati)
This session will explore how discovering and characterizing underexplored E3 ubiquitin ligases can broaden the scope of targeted protein degradation. By identifying ligases with distinct expression patterns, substrate preferences, cellular locations, and regulatory mechanisms, researchers may uncover new opportunities to degrade disease-relevant proteins that are difficult to target with currently used ligases.

Harnessing Machine Learning and Generative AI to Transform Therapeutic Discovery and Design
Session Chair: Ana Grant, DPhil (Neomorph)
These approaches can predict molecular properties, identify novel targets, and generate optimized compounds with desired efficacy, selectivity, and safety profiles. By integrating data-driven insights with experimental workflows, they improve decision-making, reduce development timelines, and expand the space of viable therapeutic candidates.

Driving Innovation in Hybrid Therapeutic Modalities (i.e., ADC, siRNA)
Session Chair: Bryan Davies, PhD (University of Texas at Austin)
Driving innovation in hybrid therapeutic modalities, such as antibody–drug conjugates (ADCs) and RNA-based platforms, focuses on combining the specificity of biologics with the potency of small molecules to create more effective treatments, as well as leveraging RNA therapeutics to silence or regulate specific gene expression.

Advancing Next-generation Drug Delivery Platforms Across Diverse Therapeutic Modalities
Session Chair: Eunji Chung, PhD (University of Southern California)
The therapeutic potential of a modality depends not only on its biological activity but also on its ability to reach the right tissues and cells at an effective concentration. This session will explore emerging drug delivery platforms designed to address these challenges across a broad range of modalities, including small molecules, biologics, peptides, and nucleic acid–based therapies.

Emerging Next-Generation Cellular Therapies
Session Chair: Sean Yamada-Hunter, PhD (UCLA)
This session will highlight emerging cellular therapies designed to overcome the limitations of current treatments and expand their use across cancer and other diseases. Topics will include engineered T-cell and natural killer cell therapies, gene-edited and allogeneic products, multi-targeting and logic-gated designs, and strategies to improve cell persistence, function, and safety.

Single-cell and spatial omics are transforming our understanding of health and disease by revealing how individual cells and the microenvironment organize, interact, maintain, and evolve within tissues. This track will highlight innovations in spatial omics technologies, functional genomics, cross-modal integration, cross-scale integration, with applications in diagnostics, therapeutics, and precision medicine. Highlights will be given to breakthroughs in epigenomics and proteomics, as well as scalable AI-based foundation models in data-driven drug discovery and high-content target identification and validation.

Functional Genomics
Session Chair: Siyuan Wang, PhD (Yale School of Medicine, Yale University)
This session will explore how diverse biological perturbations, including gene editing, epigenetic modulation, and RNA vaccines, are applied in complex, disease-relevant models. The emphasis is on functional readouts, including multimodal and high throughput approaches such as multiplexed live imaging, physiological changes, and functional protein profiling. Together, these strategies will reveal genotype-phenotype relationships, decode genetic circuits and disease signatures, map regulatory landscapes linked to disease and investigate drug-target interactions.

Emerging Spatial Omics Technologies
Session Chair: Colin Ng, MS (AtlasXomics)
This session will focus on new spatial techniques, their validation, and proof-of-principle applications across diverse biological questions. It will showcase how commercialized technologies in spatial epigenomics, transcriptomics, proteomics, and metabolomics are utilized to create high-resolution maps of human tissues in health and disease.

Omics at Scale
Session Chair: Zora Modrusan, PhD
Omics at Scale explores the industrialization of high-throughput biology, focusing on the platforms, automation, and computational infrastructure that enable large-scale omics in drug discovery and translational science. The session highlights experimental and computational advances transforming data generation across 2D and complex 3D systems and high-dimensional datasets. It also addresses challenges in standardization and scalability, emphasizing how high-plex discoveries are translated into scalable, AI-enabled, clinically deployable diagnostics and therapeutics.

Multi-omics Integration in Biomedical Discovery
Session Chair: Yuqi Tan, PhD (Stanford University)
This session will spotlight frontier technologies that fuse proteomic, transcriptomic, metabolomic, and epigenomic information into unified models of biology. Emphasis will be placed on AI-powered multimodal integration, autonomous analytical platforms, and next-generation computational approaches that are transforming our ability to predict cellular states, reconstruct tissue ecosystems, and accelerate discovery in precision medicine.

Single Cell and Spatial Omics Applications in Therapeutics
Session Chair: Johnny Yu, PhD (Tahoe Therapeutics)
This session bridges discovery and clinical translation, spotlighting how single-cell and spatial omics readouts are being embedded into drug development and therapeutic decision-making. From target identification and patient stratification to biomarker development and response prediction, talks will showcase how cellular and tissue-resolved data are moving from research tools into the practical workflows of next-generation therapeutics.

This track highlights advances in biomarker discovery, validation, and measurement across animal models, NAMs, and patient-derived samples. Topics include high-throughput and automated profiling, phenotypic and molecular signatures, and analytical approaches that connect biological readouts to disease understanding and patient outcomes. Sessions will emphasize translational use cases, including patient stratification, personalized medicine, treatment response monitoring, and the integration of biomarker endpoints into clinical trials and healthcare decision-making.

High-Throughput Strategies for Translational Discovery
Session Chair: Sara Cherry, PhD (University of Pennsylvania)
This session highlights high-throughput perturbation screening approaches for translational discovery. Topics will include CRISPR- and compound-based screens, phenotypic profiling, multi-modal readouts, and approaches to connect perturbation responses to disease-relevant signatures and therapeutic insights.

Advanced Models at Scale: Automating Complex, Physiologically Relevant Cell-Based Assays
Session Chair: Tijmen Booij, PhD (Ombion CPBT)
This session focuses on automated and scalable production of complex, physiologically relevant cell-based assays, including NAMs, 3D systems, and patient-derived models. Topics will highlight advances in automation, standardization, and throughput that enable robust assay deployment, while improving biological relevance for translational discovery and predictive modeling.

From Data to Decisions: Data Integration for Translational Insight
Session Chair: Timo Wittenberger, PhD (Genedata GmbH)
This session explores automated integration of phenotypic, molecular, and patient-derived data to drive translational discovery. Presentations will cover scalable data workflows, machine learning approaches, and methods to link complex biological signatures to disease mechanisms and therapeutic response.

The current trends in Sample Management prioritize end-to-end automation, streamlined workflows, and integration across multiple systems to improve reproducibility, data traceability, and operational efficiency. AI/ML is increasingly seen as a tool for addressing key challenges such as sample shortages, tracking, error recovery, and high-throughput processing. However, barriers such as unclear use-cases, resource constraints, and technical integration remain. Emerging modalities and expanding sample libraries further intensify the need for efficient long-term storage solutions and strategic orchestration.