MAY 21, 2025

Advancing drug discovery programs with machine learning-enhanced de novo design

De novo molecular design creates entirely new chemical entities from scratch, accelerating drug discovery by generating billions of novel molecular structures. Subsequent computational profiling of these ideas harnesses physics-based calculations and machine learning algorithms to rigorously and rapidly predict experimental endpoints for this vast chemical space.

In this webinar, we will demonstrate how large-scale de novo design workflows in Schrödinger’s AutoDesigner, combined with rigorous free energy-based scoring methods, have been applied to several recent programs to overcome critical design challenges. We will outline the use of de novo design with AutoDesigner to accelerate an EGFR discovery project, enabling the exploration of 23 billion novel chemical structures and identifying four novel scaffolds with favorable potency and property profiles in just six days. We further highlight de novo core design strategies applied to WEE1 inhibitor development, in which an automated approach generated entirely new chemotypes achieving >10,000X selectivity over PLK1 while maintaining potent target inhibition.

Finally, we introduce AutoDesigner LinkerDesign, a workflow capable of de novo generation and evaluation of billions of potential linkers between molecular fragments, further expanding computational design capabilities. We conclude with an overview of how we track the impact of these tools using interactive dashboards in LiveDesign.

Webinar Highlights

• How to design novel cores for hit identification and R-groups and linkers for hit-to-lead and lead optimization using AutoDesigner
• Examples of dramatically improving potency and selectivity in several drug discovery programs
• Requirements and best practices to apply the technology to your drug discovery programs
• Methods for tracking key performance metrics using dashboards in LiveDesign