Michael LeVine
Vice President, Modeling & Simulation Genesis Therapeutics
Seminars
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?
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.
