Human-in-the-loop active learning for goal-oriented molecule generation.

ABSTRACT: Machine learning (ML) systems have enabled the modelling of quantitative structure-property relationships (QSPR) and structure-activity relationships (QSAR) using existing experimental data to predict target properties for new molecules. These property predictors hold significant potential in accelerating drug discovery by guiding generative artificial intelligence (AI) agents to explore desired chemical spaces. However, they often struggle to generalize due to the limited scope of the training data. When optimized by generative agents, this limitation can result in the generation of molecules with artificially high predicted probabilities of satisfying target properties, which subsequently fail experimental validation. To address this challenge, we propose an adaptive approach that integr

SUBMITTER: Nahal Y 

PROVIDER: S-EPMC11629536 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature