Active learning driven prioritisation of compounds from on-demand libraries targeting the SARS-CoV-2 main protease.

ABSTRACT: FEgrow is an open-source software package for building congeneric series of compounds in protein binding pockets. For a given ligand core and receptor structure, it employs hybrid machine learning/molecular mechanics potential energy functions to optimise the bioactive conformers of supplied linkers and functional groups. Here, we introduce significant new functionality to automate, parallelise and accelerate the building and scoring of compound suggestions, such that it can be used for automated de novo design. We interface the workflow with active learning to improve the efficiency of searching the combinatorial space of possible linkers and functional groups, make use of interactions formed by crystallographic fragments in scoring compound designs, and introduce the option to see

SUBMITTER: Cree B 

PROVIDER: S-EPMC11726688 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature