Accelerating high-throughput virtual screening through molecular pool-based active learning.

ABSTRACT: Structure-based virtual screening is an important tool in early stage drug discovery that scores the interactions between a target protein and candidate ligands. As virtual libraries continue to grow (in excess of 10⁸ molecules), so too do the resources necessary to conduct exhaustive virtual screening campaigns on these libraries. However, Bayesian optimization techniques, previously employed in other scientific discovery problems, can aid in their exploration: a surrogate structure-property relationship model trained on the predicted affinities of a subset of the library can be applied to the remaining library members, allowing the least promising compounds to be excluded from evaluation. In this study, we explore the application of these techniques to computational docking da

SUBMITTER: Graff DE 

PROVIDER: S-EPMC8188596 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature