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

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

Our Speakers

Pieter Bos

Principal Scientist II, Schrödinger

Pieter Bos, Ph.D., is a principal scientist and product manager of AutoDesigner and De Novo Design workflows. At Schrödinger, his main focus is the research, development and optimization of automated compound design algorithms. Lead scientist for the design and execution of enumerated drug molecule libraries for internal and collaborative drug design projects. He received his Ph.D. in Synthetic Organic Chemistry from the University of Groningen in the laboratory of Prof. Ben Feringa. Prior to joining Schrödinger, he worked as a postdoctoral researcher in synthetic methodology development at Boston University (Prof. John Porco and Prof. Corey Stephenson) and small molecule drug discovery at Columbia University (Prof. Brent Stockwell).

Sathesh Bhat

Executive Director, Therapeutics Group, Schrödinger

Sathesh Bhat, Ph.D., executive director in the therapeutics group, joined Schrödinger in 2011. He is responsible for overseeing computational chemistry efforts on internal and partnered drug discovery programs at Schrödinger. Previously, Sathesh worked at both Merck and Eli Lilly leading computational efforts in several drug discovery programs. He obtained his Ph.D. from McGill University, which involved developing structure-based methods to predict binding free energies. Sathesh has co-authored multiple patents and publications and continues to publish on a wide variety of topics in computational chemistry.