Simulations and active learning enable efficient identification of an experimentally-validated broad coronavirus inhibitor.

ABSTRACT: Drug screening resembles finding a needle in a haystack: identifying a few effective inhibitors from a large pool of potential drugs. Large experimental screens are expensive and time-consuming, while virtual screening trades off computational efficiency and experimental correlation. Here we develop a framework that combines molecular dynamics (MD) simulations with active learning. Two components drastically reduce the number of candidates needing experimental testing to less than 20: (1) a target-specific score that evaluates target inhibition and (2) extensive MD simulations to generate a receptor ensemble. The active learning approach reduces the number of compounds requiring experimental testing to less than 10 and cuts computational costs by ∼29-fold. Using this framework, we discover

SUBMITTER: Elez K 

PROVIDER: S-EPMC12307812 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature