Alexey Rak
Head of Biostructure and Biophysics, Scientific Fellow Sanofi
Alexey Rak is Head of Biostructure and Biophysics and a Sanofi Scientific Fellow, leading global structural biology and biophysics efforts. He earned his PhD in biochemistry and biophysics and was recognized early in his career at the Max Planck Institute with the European Young Investigator Award. At Sanofi, Alexey oversees biophysics modalities including SPR, NMR, and cryo-EM, applying fragment-based and structure-driven approaches to drive hit identification, mechanistic insight, and lead optimization for small molecules and biologics. He has pioneered innovations in structural technologies, including state-of-the-art cryo-EM platforms and AI-enhanced discovery capabilities.
Seminars
Discussion Points Include:
- Explore strategies for prioritizing which biophysical assays to deploy first across diverse target classes
- Discuss how to balance high-throughput screens with deeper mechanistic assays to build confidence in early hits
- Examine how to define “confidence” in binding and progression decisions for challenging modalities, including fragments, PROTACs, intrinsically disordered proteins, and RNA-binding targets
- Share insights into managing conflicting or ambiguous data and deciding which hits to advance
This interactive, session explore the key lessons, tools, and strategies shared across the summit, and invites participants to reflect on what they will take back to their organizations. This session brings together experimentalists, computational biophysicists, and AI specialists to explore the bigger picture providing you the opportunity to have honest conversations with your industry colleagues about the biggest opportunities for biophysics to evolve across discovery, screening, and analytical development.
Key Discussion Themes:
- What did you learn that will meaningfully change your approach to biophysics, in screening, characterization, or developability?
- Which techniques, workflows, or decision-making frameworks will you apply immediately in your own programs?
- Where are the biggest gaps in current biophysical capabilities and what innovations do we want to see from vendors, academia, and internal R&D in the next 3–5 years?
- How can discovery and analytical groups collaborate more effectively to reduce friction, avoid rework, and increase translational success?
- Which complex targets or modalities remain ‘unsolved,’ and what new tools or cross-functional integrations are needed to unlock them?
Blue Sky Thinking & Audience Feedback: What does the ideal future-state biophysical toolbox look like? High-throughput? Label-free? More physiologically relevant? AI-integrated?
As biophysics workflows generate increasingly large and complex datasets, AI and machine learning are emerging as transformative tools to enhance signal extraction, automate analysis, and predict experimental outcomes. But how can we integrate these tools effectively, and responsibly, into the experimental pipeline?
This interactive workshop will bring together experts across experimental biophysics, computational modelling, and data science to explore the practical realities, opportunities, and pitfalls of implementing AI in biophysical research and drug discovery.
Join your peers to collaboratively examine:
- How AI and machine learning can streamline data processing, noise reduction, and artefact identification in high-throughput biophysical screening
- Opportunities for predictive modelling to anticipate aggregation, stability, or binding profiles
- What it means to make biophysical data AI-ready: standardization, metadata, and dataset curation
- How to validate AI-driven insights and maintain scientific rigor
- Lessons learned from early adopters applying AI to techniques like SPR, BLI, and NMR
Why Take Part?
Walk away with a practical understanding of where AI integration can genuinely accelerate discovery, and where caution, curation, and collaboration are still required.
- Address complexities of simultaneously engaging multiple targets, including aggregation, crosslinking, and kinetic heterogeneity
- Explore orthogonal biophysical approaches to fully characterize binding stoichiometry, cooperativity, and functional consequences
- Learn practical approaches to design cascades that de-risk false positives and ensure robust candidate selection
- Discuss data interpretation challenges, avidity effects, asymmetric binding, and epitope competition
