Generating 3D molecules conditional on receptor binding sites with deep generative models.

ABSTRACT: The goal of structure-based drug discovery is to find small molecules that bind to a given target protein. Deep learning has been used to generate drug-like molecules with certain cheminformatic properties, but has not yet been applied to generating 3D molecules predicted to bind to proteins by sampling the conditional distribution of protein-ligand binding interactions. In this work, we describe for the first time a deep learning system for generating 3D molecular structures conditioned on a receptor binding site. We approach the problem using a conditional variational autoencoder trained on an atomic density grid representation of cross-docked protein-ligand structures. We apply atom fitting and bond inference procedures to construct valid molecular conformations from generated atomic de

SUBMITTER: Ragoza M 

PROVIDER: S-EPMC8890264 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature