Drug-Drug Interaction Predicting by Neural Network Using Integrated Similarity.

ABSTRACT: Drug-Drug Interaction (DDI) prediction is one of the most critical issues in drug development and health. Proposing appropriate computational methods for predicting unknown DDI with high precision is challenging. We proposed "NDD: Neural network-based method for drug-drug interaction prediction" for predicting unknown DDIs using various information about drugs. Multiple drug similarities based on drug substructure, target, side effect, off-label side effect, pathway, transporter, and indication data are calculated. At first, NDD uses a heuristic similarity selection process and then integrates the selected similarities with a nonlinear similarity fusion method to achieve high-level features. Afterward, it uses a neural network for interaction prediction. The similarity selection and simila

SUBMITTER: Rohani N 

PROVIDER: S-EPMC6754439 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature