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Are you interested in speaking opportunities and being part of the conversations shaping the future of AI in drug discovery and development?
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Biology
Focus:
How AI is redefining early discovery, from target identification through validation, by improving biological insight and decision-making under uncertainty.
What to expect:
A mix of talks and live demos illustrating real-world applications, including multimodal data integration, AI-driven hypothesis generation, and how teams are embedding these tools into discovery workflows.
Takeaways:
A clearer view of where AI is delivering value today, practical patterns for implementation, and actionable ideas for scaling AI across early-stage drug discovery.
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Chemistry
Focus:
How AI is transforming molecular design and optimization, from generative chemistry to predictive modeling of properties, synthesis, and developability.
What to expect:
Talks and demos highlighting foundation models for chemistry, closed-loop design-make-test cycles, and integration of AI into medicinal chemistry workflows.
Takeaways:
Practical insights into accelerating hit-to-lead and lead optimization, and how to embed AI into chemistry teams to improve speed, quality, and decision-making.
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HPC & Infrastructure
Focus: The computational backbone required to scale AI in drug discovery, from model training to deployment across secure, enterprise-grade environments.
What to expect: Deep dives into scaling compute for foundation models, hybrid cloud and on-prem HPC architectures, and productionizing AI platforms across organizations.
Takeaways: Clear patterns for building scalable, secure infrastructure, and how to transition AI workloads from pilot to production at enterprise scale.
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Clinical Trials
Focus: Scaling AI in clinical development from experimentation to inspection-ready deployment, addressing operational and regulatory complexity.
What to expect: Perspectives and case studies on improving recruitment, increasing diversity, integrating fragmented data, and navigating evolving regulatory expectations.
Takeaways: Frameworks for governance, integration, and measurement that help teams operationalize AI in trials and demonstrate real business and patient impact.
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Tech Ops
Focus: The evolution of AI-driven manufacturing and supply chain operations, tackling variability, inefficiencies, and legacy constraints across biopharma production.
What to expect: Real-world examples of digital and AI-enabled scale-up, real-time process monitoring, and approaches to breaking down data silos across operational systems.
Takeaways: Proven strategies to enhance reliability and efficiency in manufacturing, and how to modernize Tech Ops with AI while navigating complex operational environments.
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Data Science
Focus: Building the data foundations that enable reliable, scalable AI across R&D, addressing fragmentation, quality, and integration challenges.
What to expect: Sessions exploring modern data infrastructure, advanced analytics, and approaches to harmonizing multimodal datasets for AI-driven insights.
Takeaways: Actionable approaches to improving data readiness, unlocking higher-quality insights, and supporting end-to-end AI adoption in drug discovery and development.