De novo drug design by iterative multiobjective deep reinforcement learning with graph-based molecular quality assessment.

ABSTRACT:

Motivation

Generating molecules of high quality and drug-likeness in the vast chemical space is a big challenge in the drug discovery. Most existing molecule generative methods focus on diversity and novelty of molecules, but ignoring drug potentials of the generated molecules during the generation process.

Results

In this study, we present a novel de novo multiobjective quality assessment-based drug design approach (QADD), which integrates an iterative refinement framework with a novel graph-based molecular quality assessment model on drug potentials. QADD designs a multiobjective deep reinforcement learning pipeline to generate molecules with multiple desired properties iteratively, where a graph neural network-based model for accurate molecular quality assessment on drug

SUBMITTER: Fang Y 

PROVIDER: S-EPMC10085518 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature