MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules.

ABSTRACT: Artificial intelligence (AI)-based molecular design methods, especially deep generative models for generating novel molecule structures, have gratified our imagination to explore unknown chemical space without relying on brute-force exploration. However, whether designed by AI or human experts, the molecules need to be accessibly synthesized and biologically evaluated, and the trial-and-error process remains a resources-intensive endeavor. Therefore, AI-based drug design methods face a major challenge of how to prioritize the molecular structures with potential for subsequent drug development. This study indicates that common filtering approaches based on traditional screening metrics fail to differentiate AI-designed molecules. To address this issue, we propose a novel molecular filtering

SUBMITTER: Liu X 

PROVIDER: S-EPMC10082991 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature