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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

revised on 15.03.2019, 15:31 and posted on 18.03.2019, 15:12 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.


Email Address of Submitting Author


Schrödinger LLC


United States

ORCID For Submitting Author


Declaration of Conflict of Interest

The authors declare no conflict of interest.

Version Notes

Version 1.2 (added TOC-graphic)