Abstract
Identifying potential drug-target interactions (DTIs) are crucial task for drug discovery and effective drug development. In order to address the issue, various computational methods have been widely used in drug-target interaction prediction. In this paper, we proposed a novel deep learning-based method to predict DTIs, which involved the convolutional neural networks (CNNs) to train a model and yielded robust and reliable predictions. The method achieved the accuracies of 92.0%, 90.0%, 92.0% and 90.7% on enzymes, ion channels, GPCRs and nuclear receptors in our curated dataset, respectively. The experimental results indicated that our methods improved the DTIs predictions in comparison with the state-of-the-art computational methods on the common benchmark dataset.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (Nos. 61672035, 61300058 and 61472282).
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Hu, S., Xia, D., Chen, P., Wang, B. (2018). Using Novel Convolutional Neural Networks Architecture to Predict Drug-Target Interactions. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_52
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DOI: https://doi.org/10.1007/978-3-319-95933-7_52
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