Comparison of structure- and ligand-based scoring functions for deep generative models: a GPCR case study.

ABSTRACT: Deep generative models have shown the ability to devise both valid and novel chemistry, which could significantly accelerate the identification of bioactive compounds. Many current models, however, use molecular descriptors or ligand-based predictive methods to guide molecule generation towards a desirable property space. This restricts their application to relatively data-rich targets, neglecting those where little data is available to sufficiently train a predictor. Moreover, ligand-based approaches often bias molecule generation towards previously established chemical space, thereby limiting their ability to identify truly novel chemotypes. In this work, we assess the ability of using molecular docking via Glide-a structure-based approach-as a scoring function to guide the deep generati

SUBMITTER: Thomas M 

PROVIDER: S-EPMC8117600 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature