Accelerating drug discovery with large-scale enumerations, machine learning and free-energy predictions


Thursday, May 13, 2021
10AM PDT | 1 PM EDT | 6PM BST | 7PM CEST
 

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Accelerating drug discovery with large-scale enumerations, machine learning and free-energy predictions


Thursday, May 13, 2021
10AM PDT | 1 PM EDT | 6PM BST | 7PM CEST
 

Overview

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Computational approaches are now sufficiently efficient and accurate to have significant impact on diverse facets of structure-based drug discovery campaigns. This webcast will illustrate how automated large-scale enumerations, machine learning approaches and physics-based profiling can accelerate early-stage drug discovery efforts.

The presentation will describe a synthetically-aware enumeration strategy that generates design ideas which possess project-specific physicochemical properties and which efficiently and systematically explore new IP space as well as distinct regions of the targeted binding pocket. It will also highlight best practices for active learning approaches to free energy calculations and how free energy predictions are being used at scale by drug discovery teams to accelerate project timelines.

You will learn:

  • How Schrödinger’s computational chemistry software platform can accelerate your research and development projects
  • How to improve the accuracy of in silico predictions with Schrödinger's proven methodologies
  • How integrated software solutions can streamline workflows for your drug discovery research projects

This webcast has been produced by Schrödinger, who retains sole responsibility for content. About this content.

Presenters

Presenter
Dr. Jennifer Knight
Senior Principal Scientist
Schrödinger, Inc.
View Biography
Presenter
Moderator: Sarah Hiddleston
Science Journalist
Nature Research for Nature Middle East
View Biography