ChemRxiv
These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
1/1
0/0

Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors

preprint
revised on 15.03.2019 and posted on 18.03.2019 by Kyle Konze, Pieter Bos, Markus Dahlgren, Karl Leswing, Ivan Tubert-Brohman, Andrea Bortolato, Braxton Robbason, Robert Abel, Sathesh Bhat
We report a new computational technique, PathFinder, that uses retrosynthetic analysis followed by combinatorial synthesis to generate novel compounds in synthetically accessible chemical space. Coupling PathFinder with active learning and cloud-based free energy calculations allows for large-scale potency predictions of compounds on a timescale that impacts drug discovery. The process is further accelerated by using a combination of population-based statistics and active learning techniques. Using this approach, we rapidly optimized R-groups and core hops for inhibitors of cyclin-dependent kinase 2. We explored greater than 300 thousand ideas and identified 35 ligands with diverse commercially available R-groups and a predicted IC50 < 100 nM, and four unique cores with a predicted IC50 < 100 nM. The rapid turnaround time, and scale of chemical exploration, suggests that this is a useful approach to accelerate the discovery of novel chemical matter in drug discovery campaigns.

History

Email Address of Submitting Author

sathesh.bhat@schrodinger.com

Institution

Schrödinger LLC

Country

United States

ORCID For Submitting Author

0000-0003-1819-5726

Declaration of Conflict of Interest

The authors declare no conflict of interest.

Version Notes

Version 1.2 (added TOC-graphic)

Exports