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 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 <10 nM compounds over random selection and a 1.5-fold
enrichment in identifying <10 nM compounds over our previous method,
(2) rapidly explore relevant chemical space outside the bounds of
commercial reagents, (3) use generative ML approaches to “learn”
the SAR from large scale in silico enumerations and generate novel
idea molecules for a flexible receptor site that are both potent and
within relevant physicochemical space, and (4) produce over 3 000 000
idea molecules and run 1935 FEP simulations, identifying 69 ideas
with a predicted IC50 < 10 nM and 358 ideas with a predicted
IC50 < 100 nM. The reported data suggest combining both
reaction-based and generative machine learning for ideation results
in a higher enrichment of potent compounds over previously described
approaches and has the potential to rapidly accelerate the discovery
of novel chemical matter within a predefined potency and property
space.