Limited Experimental Data? No Problem: Machine Learning and Physics in Preclinical Drug Discovery


Sathesh Bhat, Steven Jerome, and Karl Leswing
Executive Director, Sr. Principal Scientist, Research Leader


The rise of machine learning and accurate, physics-based modeling have facilitated breakthroughs in preclinical drug discovery, accelerating discovery of compounds with improved chemical properties at reduced cost relative to traditional methods.

Application of cutting-edge machine learning methods enables accurate exploration of significantly larger regions of chemical space through interpolation. Combined with accurate, extrapolative physics-based methods through active learning, hit discovery from purchasable libraries of billions of compounds becomes cost effective for the first time. Similarly, drastic expansion in chemical space searched in design-make-test-analyze cycles during lead optimization are enabled by physics-informed machine learning, resulting in speed and cost advantages.

This webcast will provide three practical examples of the application of machine learning in active drug discovery programs – one for property prediction, one for hit identification, and one for maintaining or boosting affinity through design-make-test-analyze cycles in lead optimization.