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

By Ingoo Lee, Jongsoo Keum, Hojung Nam. PLoS Comput. Biol., 2018

587 citations recorded by the source corpus.

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 demonstrate the performance of the proposed model using an independent dataset that is not seen during the training phase. As a result, our model performs better than previous protein descriptor-based models. Also, our model performs better than the recently developed deep learning models for massive prediction of DTIs. By examining pooled convolution results, we confirmed 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. Our code is available at https://github.com/GIST-CSBL/DeepConv-DTI.

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/64d025132b34770bfa43d16c1e36662af687607d

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Citations in the corpus

Artificial intelligence in cancer target identification and drug discovery (2022, background): “For example, Feng et al. 230 and Lee et al. 231 predicted DTI based on the proteins in protein-protein interaction networks with similar property features that may interact with the same drug.”
MolTrans: Molecular Interaction Transformer for drug–target interaction prediction (2020, methodology): “DeepConv-DTI [11] uses CNN and global max pooling layer to extract various length local pattern in protein sequence and applies fully connected layer on drug fingerprint ECFP4.”
Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, background): “In recently published works [ 116–122], methods such as deep belief neural networks [118, 119], convolutional neural networks [120, 122] and multiple layer perceptrons [121, 122] were used to establish DTI prediction programs.”
Interpretable bilinear attention network with domain adaptation improves drug–target prediction (2022, unknown): “LadvPF,G,D) P EExt i ∼SSt logP1 − DPf t i ,g i )) + EExs j ∼SSs logPDPf s j ,g s j )), (11)”