  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
are shown to be not informative enough to predict accurate DTIs. Thus, in this
study, we employ a convolutional neural network (CNN) on raw protein sequences
to capture local residue patterns participating in DTIs. With CNN on protein
sequences, our model performs better than previous protein descriptor-based
models. In addition, our model performs better than the previous deep learning
model for massive prediction of DTIs. By examining the pooled convolution
results, we found that our model can detect binding sites of proteins for DTIs.
In conclusion, our prediction model for detecting local residue patterns of
target proteins successfully enriches the protein features of a raw protein
sequence, yielding better prediction results than previous approaches.
