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

Speakers

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

Abstract

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.