MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning.

ABSTRACT: The joint use of multiple drugs may cause unintended drug-drug interactions (DDIs) and result in adverse consequence to the patients. Accurate identification of DDI types can not only provide hints to avoid these accidental events, but also elaborate the underlying mechanisms by how DDIs occur. Several computational methods have been proposed for multi-type DDI prediction, but room remains for improvement in prediction performance. In this study, we propose a supervised contrastive learning based method, MDDI-SCL, implemented by three-level loss functions, to predict multi-type DDIs. MDDI-SCL is mainly composed of three modules: drug feature encoder and mean squared error loss module, drug latent feature fusion and supervised contrastive loss module, multi-type DDI prediction and classific

SUBMITTER: Lin S 

PROVIDER: S-EPMC9667597 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature