){kind=logo}
){kind=logo}
Premium Pass
-
Speakers
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Speakers
-
-
Speakers
-
-
-
-
Speakers
-
Speakers
-
-
-
-
-
-
-
Speakers
-
-
Speakers
-
-
-
-
-
-
-
-
-
-
Analyze what it takes to deploy AI in environments where validation, traceability, access control and oversight matter more. Consider how regulated use cases change expectations around infrastructure …
-
Data
-
-
• Examine how AI models support the design of bifunctional molecules for targeted protein degradation.• Explore strategies for predicting ternary complex formation, linker optimization and degrader se …
-
Investigate how AI integrates genomic, proteomic and clinical signals to uncover target-linked biomarkers. Assess how multimodal analytics are supporting earlier and more precise stratification hypoth …
-
Data
-
TechOps
-
-
-
-
-
Consider how organizations are segmenting workloads across on-prem HPC, elastic cloud and specialized GPU environments. Review which drug discovery and development workloads benefit most from hybrid d …
-
-
-
-
Data
-
-
-
-
Learning Outcomes: How near real-time data infrastructure can transform clinical trial execution and decision-making Key challenges in integrating data across sponsors, CROs, and vendors—and strategie …
-
Speakers
-
-
Lambda Labs How Lambda is powering open source model training to unlock drug discovery breakthroughs
-
Evaluate where AI has improved target identification and validation by strengthening clinically relevant hypotheses, and where it has failed to translate into better discovery decisions. Identify the …
-
-
Data
-
-
Regulatory agencies are rapidly advancing their thinking on AI in clinical trials, but expectations are still evolving across regions and use cases. That uncertainty is showing up in differing validat …
-
TechOps
-
-
R&D teams often work in silos prototyping AI capabilities for narrow use cases Prototypes and pilots are very fit for purpose and often do not share architectures even for similar use cases In this ta …
-
Examine how AI integrates single-cell and spatial data to map cellular states and tissue architecture in disease. Explore how cellular heterogeneity and microenvironment context influence target local …
-
-
-
Discuss how biopharma organizations are moving beyond fragmented AI pilots to build shared, enterprise-grade compute environments for discovery and development. Compare the strategic choices between c …
-
-
-
• Examine how foundation models predict structure, function and interactions directly from sequence data. • Explore how cross-modal learning links sequence, structure and phenotype to generate target …
-
Speakers
-
AI adoption is accelerating and organizations are facing increasingly complex build vs. buy decisions, with no one-size-fits-all approach even possible. This is causing a friction that is showing up i …
-
Data
-
-
Examine how autonomous AI systems are proposing and refining experimental hypotheses in real time. Understand how closed-loop experimentation integrates computational prediction with iterative biologi …
-
Analyze how biopharma leaders are deciding when to own infrastructure, rely on hyperscalers or work with specialist compute partners. Weigh the trade-offs across talent, speed, capital efficiency, res …
-
-
Analyze how AI maps high-dimensional SAR to guide molecular refinement. Investigate strategies for balancing potency, selectivity and developability simultaneously. Evaluate how predictive prioritizat …
-
-
-
-
-
• Understand how advanced ML models deliver increasingly reliable ADME, PK and developability predictions.• Examine multi-parameter optimization frameworks that integrate permeability, stability, solu …
-
-
-
As AI becomes embedded in clinical development, accountability is becoming harder to define. This is especially true when decisions are partially or fully influenced by algorithms. Ambiguity is showin …
-
-
TechOpsSpeakers
-
-
-
Discuss how pharma organizations are broadening compute strategy from research into development, technical operations and enterprise deployment. Compare where infrastructure priorities stay the same a …
-
Data
-
• Define what AI-enabled biologics design means in practice at Takeda.• Explore structure-informed and structure-free approaches to biologics design.• Assess how AI can compress design-make-test cycle …
-
-
-
-
-
-
-
TechOps
-
DataExplore how robust biological data management, governance and integration strategies can enable AI-driven target identification across discovery workflows. Model how structured genomic, functional and …
-
Assess how AI can integrate multi-modal data to reveal actionable insights across heterogeneous patient populations. Understand how factors such as disease subtypes and treatment-response variability …
-
AI ambitions are often limited by fragmented, study-level data that isn’t designed for reuse or scale. Siloed systems, inconsistent metadata, and limited ability to generate cross-study insights are a …
-
-
-
Analyze how structural AI is advancing understanding of protein interactions, conformational states and functional modulation. Consider how structure-informed modeling is refining druggability assessm …
-
-
-
-
Analyze how growing demand for model training, inference and simulation is reshaping GPU infrastructure strategies across pharma R&D. Explore what it takes to scale GPU environments effectively, inclu …
-
Data
-
-
-
-
Discuss how lab infrastructure is evolving to support real-time data capture, model-driven experimentation and faster decision-making. Explore how instrument connectivity, workflow orchestration and l …
-
-
-
• Assess how multimodal data integration reveals novel disease–target–compound relationships.• Explore network-based and foundation model approaches for identifying new therapeutic indications.• Evalu …
-
AI investment in clinical trials is accelerating, but clear, measurable returns are still difficult to define. Pharma/Biotech companies are waiting to see if there is an ROI to shed some uncertainty o …
-
-
-
-
-
-
-
-
-
Deploying AI in clinical setting is just the first hurdle; maintaining performance, compliance and trust over time has to be successful. This complexity is showing up in model drift, unclear re-valida …
-
-
-
Analyze how leading organizations are redesigning scientific computing platforms around the needs of researchers rather than infrastructure teams. Consider how self-service workflows, preconfigured en …
-
Analyze how error propagation, tool misuse and cascading failures emerge in multi-agentic AI systems. Explore the guardrails needed to support reliable deployment, including orchestration controls, hu …
-
-
-
-
-
- Discuss how AI is reshaping the overall paradigm of molecular design and optimization in modern drug discovery.- Explore the challenges of scaling AI from promising tools to fully integrated discove …
-
-
-
-
-
Assess how high-content imaging and multi-omic integration are uncovering functional insights beyond single-gene analyses. Explore how AI-driven phenotypic profiling is clarifying mechanism of action …
-
• Examine how AI is clarifying mechanism of action by integrating pathway biology, perturbation data and disease context.• Explore how deeper mechanistic insight is shaping target differentiation and …
-
TechOps
-
-
Examine how data engineering challenges are changing as AI, simulation and multimodal workflows place greater demands on scientific infrastructure. Explore how organizations are managing storage tiers …
-
-
-
TechOps
-
-
AI deployment in early stage clinical trials and impact on commercial
-
-
Examine how ML-augmented docking and foundation models are improving hit identification accuracy. Explore strategies for screening billion- to trillion-scale virtual libraries efficiently. Assess how …
-
Examine how compute-intensive modelling, simulation and AI are being applied in process development and manufacturing environments. Review where infrastructure is needed to support scale-up, process u …
-
AI is being embedded into GxP workflows, but organizations still face challenges around validation, lifecycle management, and cross-functional governance. DiMe will lead the discussion covering: What …
-
TechOps
-
AI is moving rapidly from pilot use cases into core clinical trial decision-making, but most organizations are still not structured to capture consistent, scalable value. That gap is showing up in fra …
-
-
-
Data
-
-
-
-
Examine how generative AI systems are proposing mechanistic target hypotheses grounded in multimodal data. Explore how hypothesis-generation models complement human expertise in identifying novel biol …Speakers
-
• Explore how AI models help identify potential safety risks earlier in the drug discovery process.• Examine advances in predictive modeling for assessing compound liabilities alongside potency and de …
-
• Investigate advances in reaction prediction, condition optimization and yield estimation using data-driven approaches• AI Agents and humans working hand in hand on reaction data• Future of synthesis …
-
-
-
Explore how multimodal workloads are changing infrastructure needs across development as well as discovery. Discuss the compute, storage and orchestration challenges involved in supporting imaging-hea …
-
• Discuss how organizations are evaluating the real-world impact of AI on drug discovery productivity and success rates.• Examine which metrics matter most when assessing AI performance across hit dis …
-
-
-
-
Examine how foundation models learn chemical and reaction language at scale. Explore how LLMs enable de novo design, retrosynthesis and reaction prediction. Assess how these systems are improving desi …
-
Examine how AI refines binding poses and predicts protein–ligand interactions beyond classical docking. Explore fragment growing, scaffold hopping and structure-guided hit expansion strategies. Assess …
-
-
Review how biological, chemical and multimodal foundation models are changing infrastructure requirements across training, tuning and inference. Clarify the implications for storage tiers, interconnec …
-
Clinical trials are technical and expensive endeavours and therefore require extensive planning. AI agents are beginning to need to self evolve to catch up, but what does this look like Attendees can …
-
-
-
Clinical trials succeed when patients can clearly articulate their experience, understand their disease, and engage as partners and yet rare disease populations often struggle to do exactly that. Pati …
-
• Examine how AI analyzes large-scale CRISPR and Perturb-seq screens to distinguish causal drivers from correlative signals.• Explore how combinatorial perturbation modelling uncovers context-specific …
-
-
-
• How large-scale functional and omics datasets, interrogated through systematic unbiased approaches, can reveal high-confidence oncology targets • Real-world examples of targets identified through th …
-
Examine how generative models enable direct in silico molecular design across modalities. Explore how agentic systems and lab-in-the-loop learning create closed-loop chemistry platforms. Assess how en …
-
-
-
• Understand how integrated Design–Make–Test–Analyze platforms enable rapid, iterative molecular optimization.• Analyze the role of robotics, real-time analytics and active learning in autonomous expe …
-
Understand how predictive and generative models are enabling antibody, peptide and protein sequence design and optimization. Analyze how structure-aware AI is improving affinity, stability, specificit …
-
Track how modern HPC environments are supporting molecular dynamics, free-energy methods and structure-based workflows at greater scale. Demonstrate how physics-based simulation is being combined with …
-
-
Examine how leading organizations are shifting to AI-first target identification and embedding multimodal models into core discovery workflows. Assess how causal systems biology and integrated data pl …
-
-
-
-
-
-
-
Premium Pass
-
Speakers
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Speakers
-
-
Speakers
-
-
-
-
Speakers
-
Speakers
-
-
-
-
-
-
-
Speakers
-
-
Speakers
-
-
-
-
-
-
-
-
-
-
Analyze what it takes to deploy AI in environments where validation, traceability, access control and oversight matter more. Consider how regulated use cases change expectations around infrastructure …
-
Data
-
-
• Examine how AI models support the design of bifunctional molecules for targeted protein degradation.• Explore strategies for predicting ternary complex formation, linker optimization and degrader se …
-
Investigate how AI integrates genomic, proteomic and clinical signals to uncover target-linked biomarkers. Assess how multimodal analytics are supporting earlier and more precise stratification hypoth …
-
Data
-
TechOps
-
-
-
-
-
Consider how organizations are segmenting workloads across on-prem HPC, elastic cloud and specialized GPU environments. Review which drug discovery and development workloads benefit most from hybrid d …
-
-
-
-
Data
-
-
-
-
Learning Outcomes: How near real-time data infrastructure can transform clinical trial execution and decision-making Key challenges in integrating data across sponsors, CROs, and vendors—and strategie …
-
Speakers
-
-
Lambda Labs How Lambda is powering open source model training to unlock drug discovery breakthroughs
-
Evaluate where AI has improved target identification and validation by strengthening clinically relevant hypotheses, and where it has failed to translate into better discovery decisions. Identify the …
-
-
Data
-
-
Regulatory agencies are rapidly advancing their thinking on AI in clinical trials, but expectations are still evolving across regions and use cases. That uncertainty is showing up in differing validat …
-
TechOps
-
-
R&D teams often work in silos prototyping AI capabilities for narrow use cases Prototypes and pilots are very fit for purpose and often do not share architectures even for similar use cases In this ta …
-
Examine how AI integrates single-cell and spatial data to map cellular states and tissue architecture in disease. Explore how cellular heterogeneity and microenvironment context influence target local …
-
-
-
Discuss how biopharma organizations are moving beyond fragmented AI pilots to build shared, enterprise-grade compute environments for discovery and development. Compare the strategic choices between c …
-
-
-
• Examine how foundation models predict structure, function and interactions directly from sequence data. • Explore how cross-modal learning links sequence, structure and phenotype to generate target …
-
Speakers
-
AI adoption is accelerating and organizations are facing increasingly complex build vs. buy decisions, with no one-size-fits-all approach even possible. This is causing a friction that is showing up i …
-
Data
-
-
Examine how autonomous AI systems are proposing and refining experimental hypotheses in real time. Understand how closed-loop experimentation integrates computational prediction with iterative biologi …
-
Analyze how biopharma leaders are deciding when to own infrastructure, rely on hyperscalers or work with specialist compute partners. Weigh the trade-offs across talent, speed, capital efficiency, res …
-
-
Analyze how AI maps high-dimensional SAR to guide molecular refinement. Investigate strategies for balancing potency, selectivity and developability simultaneously. Evaluate how predictive prioritizat …
-
-
-
-
-
• Understand how advanced ML models deliver increasingly reliable ADME, PK and developability predictions.• Examine multi-parameter optimization frameworks that integrate permeability, stability, solu …
-
-
-
As AI becomes embedded in clinical development, accountability is becoming harder to define. This is especially true when decisions are partially or fully influenced by algorithms. Ambiguity is showin …
-
-
TechOpsSpeakers
-
-
-
Discuss how pharma organizations are broadening compute strategy from research into development, technical operations and enterprise deployment. Compare where infrastructure priorities stay the same a …
-
Data
-
• Define what AI-enabled biologics design means in practice at Takeda.• Explore structure-informed and structure-free approaches to biologics design.• Assess how AI can compress design-make-test cycle …
-
-
-
-
-
-
-
TechOps
-
DataExplore how robust biological data management, governance and integration strategies can enable AI-driven target identification across discovery workflows. Model how structured genomic, functional and …
-
Assess how AI can integrate multi-modal data to reveal actionable insights across heterogeneous patient populations. Understand how factors such as disease subtypes and treatment-response variability …
-
AI ambitions are often limited by fragmented, study-level data that isn’t designed for reuse or scale. Siloed systems, inconsistent metadata, and limited ability to generate cross-study insights are a …
-
-
-
Analyze how structural AI is advancing understanding of protein interactions, conformational states and functional modulation. Consider how structure-informed modeling is refining druggability assessm …
-
-
-
-
Analyze how growing demand for model training, inference and simulation is reshaping GPU infrastructure strategies across pharma R&D. Explore what it takes to scale GPU environments effectively, inclu …
-
Data
-
-
-
-
Discuss how lab infrastructure is evolving to support real-time data capture, model-driven experimentation and faster decision-making. Explore how instrument connectivity, workflow orchestration and l …
-
-
-
• Assess how multimodal data integration reveals novel disease–target–compound relationships.• Explore network-based and foundation model approaches for identifying new therapeutic indications.• Evalu …
-
AI investment in clinical trials is accelerating, but clear, measurable returns are still difficult to define. Pharma/Biotech companies are waiting to see if there is an ROI to shed some uncertainty o …
-
-
-
-
-
-
-
-
-
Deploying AI in clinical setting is just the first hurdle; maintaining performance, compliance and trust over time has to be successful. This complexity is showing up in model drift, unclear re-valida …
-
-
-
Analyze how leading organizations are redesigning scientific computing platforms around the needs of researchers rather than infrastructure teams. Consider how self-service workflows, preconfigured en …
-
Analyze how error propagation, tool misuse and cascading failures emerge in multi-agentic AI systems. Explore the guardrails needed to support reliable deployment, including orchestration controls, hu …
-
-
-
-
-
- Discuss how AI is reshaping the overall paradigm of molecular design and optimization in modern drug discovery.- Explore the challenges of scaling AI from promising tools to fully integrated discove …
-
-
-
-
-
Assess how high-content imaging and multi-omic integration are uncovering functional insights beyond single-gene analyses. Explore how AI-driven phenotypic profiling is clarifying mechanism of action …
-
• Examine how AI is clarifying mechanism of action by integrating pathway biology, perturbation data and disease context.• Explore how deeper mechanistic insight is shaping target differentiation and …
-
TechOps
-
-
Examine how data engineering challenges are changing as AI, simulation and multimodal workflows place greater demands on scientific infrastructure. Explore how organizations are managing storage tiers …
-
-
-
TechOps
-
-
AI deployment in early stage clinical trials and impact on commercial
-
-
Examine how ML-augmented docking and foundation models are improving hit identification accuracy. Explore strategies for screening billion- to trillion-scale virtual libraries efficiently. Assess how …
-
Examine how compute-intensive modelling, simulation and AI are being applied in process development and manufacturing environments. Review where infrastructure is needed to support scale-up, process u …
-
AI is being embedded into GxP workflows, but organizations still face challenges around validation, lifecycle management, and cross-functional governance. DiMe will lead the discussion covering: What …
-
TechOps
-
AI is moving rapidly from pilot use cases into core clinical trial decision-making, but most organizations are still not structured to capture consistent, scalable value. That gap is showing up in fra …
-
-
-
Data
-
-
-
-
Examine how generative AI systems are proposing mechanistic target hypotheses grounded in multimodal data. Explore how hypothesis-generation models complement human expertise in identifying novel biol …Speakers
-
• Explore how AI models help identify potential safety risks earlier in the drug discovery process.• Examine advances in predictive modeling for assessing compound liabilities alongside potency and de …
-
• Investigate advances in reaction prediction, condition optimization and yield estimation using data-driven approaches• AI Agents and humans working hand in hand on reaction data• Future of synthesis …
-
-
-
Explore how multimodal workloads are changing infrastructure needs across development as well as discovery. Discuss the compute, storage and orchestration challenges involved in supporting imaging-hea …
-
• Discuss how organizations are evaluating the real-world impact of AI on drug discovery productivity and success rates.• Examine which metrics matter most when assessing AI performance across hit dis …
-
-
-
-
Examine how foundation models learn chemical and reaction language at scale. Explore how LLMs enable de novo design, retrosynthesis and reaction prediction. Assess how these systems are improving desi …
-
Examine how AI refines binding poses and predicts protein–ligand interactions beyond classical docking. Explore fragment growing, scaffold hopping and structure-guided hit expansion strategies. Assess …
-
-
Review how biological, chemical and multimodal foundation models are changing infrastructure requirements across training, tuning and inference. Clarify the implications for storage tiers, interconnec …
-
Clinical trials are technical and expensive endeavours and therefore require extensive planning. AI agents are beginning to need to self evolve to catch up, but what does this look like Attendees can …
-
-
-
Clinical trials succeed when patients can clearly articulate their experience, understand their disease, and engage as partners and yet rare disease populations often struggle to do exactly that. Pati …
-
• Examine how AI analyzes large-scale CRISPR and Perturb-seq screens to distinguish causal drivers from correlative signals.• Explore how combinatorial perturbation modelling uncovers context-specific …
-
-
-
• How large-scale functional and omics datasets, interrogated through systematic unbiased approaches, can reveal high-confidence oncology targets • Real-world examples of targets identified through th …
-
Examine how generative models enable direct in silico molecular design across modalities. Explore how agentic systems and lab-in-the-loop learning create closed-loop chemistry platforms. Assess how en …
-
-
-
• Understand how integrated Design–Make–Test–Analyze platforms enable rapid, iterative molecular optimization.• Analyze the role of robotics, real-time analytics and active learning in autonomous expe …
-
Understand how predictive and generative models are enabling antibody, peptide and protein sequence design and optimization. Analyze how structure-aware AI is improving affinity, stability, specificit …
-
Track how modern HPC environments are supporting molecular dynamics, free-energy methods and structure-based workflows at greater scale. Demonstrate how physics-based simulation is being combined with …
-
-
Examine how leading organizations are shifting to AI-first target identification and embedding multimodal models into core discovery workflows. Assess how causal systems biology and integrated data pl …
-
-
-
-
-
-
-