James Bowman

Artificial intelligence and machine learning are transforming the way biophysical data is generated, interpreted, and applied. But beyond the day-to-day integration of AI into experimental workflows, what are the broader opportunities, challenges, and limitations?

This interactive panel brings together leading experimentalists, computational biophysicists, and AI specialists to explore the bigger picture and provides you the opportunity to have honest conversations with your industry colleagues about hype vs. value.

Key Discussion Themes:

• Where can AI add true value in biophysics beyond data processing? Predictive modelling, hit triaging, protein design?
• Which areas of drug discovery are most ready for AI adoption and where should caution succeed?
• The challenges of AI adoption including data curation, standardization, reproducibility, interpretability, and integration with lab workflows
• The evolving role of experimental validation alongside AI predictions, how to ensure we maintain scientific rigor

Blue Sky Thinking & Audience Feedback: In 5 years, what do you hope AI/ML has enabled / transformed the way you do something?

• Explore how generative AI models can predict molecular behaviour and biophysical properties from protein stability, ligand-binding trends, and formulation success
• Understand challenges and best practices for validating AI predictions in wet lab experiments
• Explore real-world case study of how AI is accelerating discovery in biophysics