David Brown
Senior Director, Structural Biology & Biophysics Vertex Pharmaceuticals
A founding speaker at the Structure-Based Drug Design Summit, David has been shaping the field of rational drug design across every iteration of the event. Currently Senior Director at Vertex Pharmaceuticals and Professor at the University of Kent, he leads structural biology and biophysics initiatives to guide drug discovery programs. With a career spanning Pfizer, Servier, and advisory roles for large-scale synchrotron facilities, David has applied NMR, X-ray crystallography, mass spectrometry, and computational modeling to advance therapeutics across multiple disease areas, including antivirals, cardiovascular, and tissue repair.
Seminars
As the toolbox of techniques and modalities continues to expand, structure-based drug design is at a crossroads. This interactive panel invites the audience and expert panellists to reflect on the most pressing challenges, biggest opportunities, and future directions to maximize the impact of structural insights on drug discovery.
Key Discussion Points:
- What are the key bottlenecks still limiting the impact of SBDD (resolution, dynamics, integration, translation to clinic)?
- Which emerging tools (AI, cryo-ET, integrative modelling, real-time structural methods) will reshape the field in the next 5 years?
- How can we bridge the gap between structural data and actionable design decisions for both small molecules and biologics?
- What collaborations, technologies, or standards are still needed to make SBDD more predictive and accessible?
With AI tools like AlphaFold3, Boltz-2, Chai-1, RFdiffusion and generative chemistry platforms moving rapidly into the drug discovery space, the question is no longer if AI belongs in structure-based drug design but how to apply it effectively. This workshop will equip attendees with practical insights on incorporating AI/ML into the structural biology workflow, ensuring models are reliable, interpretable, and complementary to experimental approaches.
Highlights include:
Understand the current capabilities and limitations of AI for protein structure prediction, binding mode analysis, and property optimization
Learn strategies to integrate AI predictions with X-ray crystallography, Cryo-EM, NMR, and biophysical assays to validate and refine outputs
Explore case studies demonstrating AI in action, from accelerating hit-to-lead optimization to enabling novel scaffold and biologics design
Discuss best practices for data quality, avoiding “garbage in, garbage out,” and building trust in AI models across discovery teams
This session is ideal for structural biologists, computational chemists, AI/ML specialists, and drug discovery scientists seeking to unlock the full potential of artificial intelligence as part of a cohesive, structure-driven design strategy.
