ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular Generation.

ABSTRACT: The incredible capabilities of generative artificial intelligence models have inevitably led to their application in the domain of drug discovery. Within this domain, the vastness of chemical space motivates the development of more efficient methods for identifying regions with molecules that exhibit desired characteristics. In this work, we present a computationally efficient active learning methodology that requires evaluation of only a subset of the generated data in the constructed sample space to successfully align a generative model with respect to a specified objective. We demonstrate the applicability of this methodology to targeted molecular generation by fine-tuning a GPT-based molecular generator toward a protein with FDA-approved small-molecule inhibitors, c-Abl kinase. Remarka

SUBMITTER: Kyro GW 

PROVIDER: S-EPMC10516108 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature