Chemical Space Exploration with Active Learning and Alchemical Free Energies.

ABSTRACT: Drug discovery can be thought of as a search for a needle in a haystack: searching through a large chemical space for the most active compounds. Computational techniques can narrow the search space for experimental follow up, but even they become unaffordable when evaluating large numbers of molecules. Therefore, machine learning (ML) strategies are being developed as computationally cheaper complementary techniques for navigating and triaging large chemical libraries. Here, we explore how an active learning protocol can be combined with first-principles based alchemical free energy calculations to identify high affinity phosphodiesterase 2 (PDE2) inhibitors. We first calibrate the procedure using a set of experimentally characterized PDE2 binders. The optimized protocol is then used prosp

SUBMITTER: Khalak Y 

PROVIDER: S-EPMC9558370 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature