Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach.

ABSTRACT: Prediction of the interactions between small molecules and their targets play important roles in various applications of drug development, such as lead discovery, drug repurposing and elucidation of potential drug side effects. Therefore, a variety of machine learning-based models have been developed to predict these interactions. In this study, a model called auxiliary multi-task graph isomorphism network with uncertainty weighting (AMGU) was developed to predict the inhibitory activities of small molecules against 204 different kinases based on the multi-task Graph Isomorphism Network (MT-GIN) with the auxiliary learning and uncertainty weighting strategy. The calculation results illustrate that the AMGU model outperformed the descriptor-based models and state-of-the-art graph neural net

SUBMITTER: Bao L 

PROVIDER: S-EPMC9939366 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature