Novel deep learning model for more accurate prediction of drug-drug interaction effects.

ABSTRACT:

Background

Predicting the effect of drug-drug interactions (DDIs) precisely is important for safer and more effective drug co-prescription. Many computational approaches to predict the effect of DDIs have been proposed, with the aim of reducing the effort of identifying these interactions in vivo or in vitro, but room remains for improvement in prediction performance.

Results

In this study, we propose a novel deep learning model to predict the effect of DDIs more accurately.. The proposed model uses autoencoders and a deep feed-forward network that are trained using the structural similarity profiles (SSP), Gene Ontology (GO) term similarity profiles (GSP), and target gene similarity profiles (TSP) of known drug pairs to predict the pharmacological effects of DDIs. The resul

SUBMITTER: Lee G 

PROVIDER: S-EPMC6685287 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature