Combining Cloud-Based Free-Energy Calculations, Synthetically Aware Enumerations, and Goal-Directed Generative Machine Learning for Rapid Large-Scale Chemical Exploration and Optimization

The hit identification process usually involves the profiling of millions to more recently billions of compounds either via traditional experimental high-throughput screens (HTS) or computational virtual high-throughput screens (vHTS). We have previously demonstrated that, by coupling reaction-based...

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Bibliographic Details
Published in:Journal of chemical information and modeling 2020-09, Vol.60 (9), p.4311-4325
Main Authors: Ghanakota, Phani, Bos, Pieter H, Konze, Kyle D, Staker, Joshua, Marques, Gabriel, Marshall, Kyle, Leswing, Karl, Abel, Robert, Bhat, Sathesh
Format: Article
Language:English
Subjects:
Computational Chemistry
Coupling (molecular)
Cyclin-dependent kinases
Enrichment
Enumeration
Free energy
Kinases
Machine learning
Mathematical analysis
Optimization
Perturbation
Reagents
Screens
Workflow
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Summary:The hit identification process usually involves the profiling of millions to more recently billions of compounds either via traditional experimental high-throughput screens (HTS) or computational virtual high-throughput screens (vHTS). We have previously demonstrated that, by coupling reaction-based enumeration, active learning, and free energy calculations, a similarly large-scale exploration of chemical space can be extended to the hit-to-lead process. In this work, we augment that approach by coupling large scale enumeration and cloud-based free energy perturbation (FEP) profiling with goal-directed generative machine learning, which results in a higher enrichment of potent ideas compared to large scale enumeration alone, while simultaneously staying within the bounds of predefined drug-like property space. We can achieve this by building the molecular distribution for generative machine learning from the PathFinder rules-based enumeration and optimizing for a weighted sum QSAR-based multiparameter optimization function. We examine the utility of this combined approach by designing potent inhibitors of cyclin-dependent kinase 2 (CDK2) and demonstrate a coupled workflow that can (1) provide a 6.4-fold enrichment improvement in identifying
ISSN:1549-9596
1549-960X
DOI:10.1021/acs.jcim.0c00120