DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences.

ABSTRACT: Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In several computational models, conventional protein descriptors have been shown to not be sufficiently informative to predict accurate DTIs. Thus, in this study, we propose a deep learning based DTI prediction model capturing local residue patterns of proteins participating in DTIs. When we employ a convolutional neural network (CNN) on raw protein sequences, we perform convolution on various lengths of amino acids subsequences to capture local residue patterns of generalized protein classes. We train our model with large-scale DTI information and dem

SUBMITTER: Lee I 

PROVIDER: S-EPMC6594651 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature