Molecule generation using transformers and policy gradient reinforcement learning.

ABSTRACT: Generating novel valid molecules is often a difficult task, because the vast chemical space relies on the intuition of experienced chemists. In recent years, deep learning models have helped accelerate this process. These advanced models can also help identify suitable molecules for disease treatment. In this paper, we propose Taiga, a transformer-based architecture for the generation of molecules with desired properties. Using a two-stage approach, we first treat the problem as a language modeling task of predicting the next token, using SMILES strings. Then, we use reinforcement learning to optimize molecular properties such as QED. This approach allows our model to learn the underlying rules of chemistry and more easily optimize for molecules with desired properties. Our evaluation of T

SUBMITTER: Mazuz E 

PROVIDER: S-EPMC10232454 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature