Applying atomistic neural networks to bias conformer ensembles towards bioactive-like conformations.

ABSTRACT: Identifying bioactive conformations of small molecules is an essential process for virtual screening applications relying on three-dimensional structure such as molecular docking. For most small molecules, conformer generators retrieve at least one bioactive-like conformation, with an atomic root-mean-square deviation (ARMSD) lower than 1 Å, among the set of low-energy conformers generated. However, there is currently no general method to prioritise these likely target-bound conformations in the ensemble. In this work, we trained atomistic neural networks (AtNNs) on 3D information of generated conformers of a curated subset of PDBbind ligands to predict the ARMSD to their closest bioactive conformation, and evaluated the early enrichment of bioactive-like conformations when ranking conform

SUBMITTER: Baillif B 

PROVIDER: S-EPMC10740246 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature