Artificial intelligence for prediction of biological activities and generation of molecular hits using stereochemical information.

ABSTRACT: In this work, we develop a method for generating targeted hit compounds by applying deep reinforcement learning and attention mechanisms to predict binding affinity against a biological target while considering stereochemical information. The novelty of this work is a deep model Predictor that can establish the relationship between chemical structures and their corresponding [Formula: see text] values. We thoroughly study the effect of different molecular descriptors such as ECFP4, ECFP6, SMILES and RDKFingerprint. Also, we demonstrated the importance of attention mechanisms to capture long-range dependencies in molecular sequences. Due to the importance of stereochemical information for the binding mechanism, this information was employed both in the prediction and generation processes. T

SUBMITTER: Pereira TO 

PROVIDER: S-EPMC10618333 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature