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.