Diversity oriented Deep Reinforcement Learning for targeted molecule generation.

ABSTRACT: In this work, we explore the potential of deep learning to streamline the process of identifying new potential drugs through the computational generation of molecules with interesting biological properties. Two deep neural networks compose our targeted generation framework: the Generator, which is trained to learn the building rules of valid molecules employing SMILES strings notation, and the Predictor which evaluates the newly generated compounds by predicting their affinity for the desired target. Then, the Generator is optimized through Reinforcement Learning to produce molecules with bespoken properties. The innovation of this approach is the exploratory strategy applied during the reinforcement training process that seeks to add novelty to the generated compounds. This training strat

SUBMITTER: Pereira T 

PROVIDER: S-EPMC7944916 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature