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

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…

• NR
  National Research Foundation
• MO
  Ministry of Science, ICT and Future Planning

  Award: NRF-2018M3A9A7053266.
• NR
  National Research Foundation of Korea

  Award: NRF-2018M3A9A7053266
• MO
  Ministry of Science and ICT, South Korea