Evaluation of reinforcement learning in transformer-based molecular design.

ABSTRACT: Designing compounds with a range of desirable properties is a fundamental challenge in drug discovery. In pre-clinical early drug discovery, novel compounds are often designed based on an already existing promising starting compound through structural modifications for further property optimization. Recently, transformer-based deep learning models have been explored for the task of molecular optimization by training on pairs of similar molecules. This provides a starting point for generating similar molecules to a given input molecule, but has limited flexibility regarding user-defined property profiles. Here, we evaluate the effect of reinforcement learning on transformer-based molecular generative models. The generative model can be considered as a pre-trained model with knowledge of the

SUBMITTER: He J 

PROVIDER: S-EPMC11312936 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature