Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks.

ABSTRACT:

Background

Accurate identification of potential interactions between drugs and protein targets is a critical step to accelerate drug discovery. Despite many relative experimental researches have been done in the past decades, detecting drug-target interactions (DTIs) remains to be extremely resource-intensive and time-consuming. Therefore, many computational approaches have been developed for predicting drug-target associations on a large scale.

Results

In this paper, we proposed an deep learning-based method to predict DTIs only using the information of drug structures and protein sequences. The final results showed that our method can achieve good performance with the accuracies up to 92.0%, 90.0%, 92.0% and 90.7% for the target families of enzymes, ion channels, GPCRs and

SUBMITTER: Hu S 

PROVIDER: S-EPMC6929541 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature