DeSIDE-DDI: interpretable prediction of drug-drug interactions using drug-induced gene expressions.

ABSTRACT: Adverse drug-drug interaction (DDI) is a major concern to polypharmacy due to its unexpected adverse side effects and must be identified at an early stage of drug discovery and development. Many computational methods have been proposed for this purpose, but most require specific types of information, or they have less concern in interpretation on underlying genes. We propose a deep learning-based framework for DDI prediction with drug-induced gene expression signatures so that the model can provide the expression level of interpretability for DDIs. The model engineers dynamic drug features using a gating mechanism that mimics the co-administration effects by imposing attention to genes. Also, each side-effect is projected into a latent space through translating embedding. As a result, the

SUBMITTER: Kim E 

PROVIDER: S-EPMC8895921 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature