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Using Novel Convolutional Neural Networks Architecture to Predict Drug-Target Interactions

  • ShanShan Hu
  • DeNan Xia
  • Peng Chen
  • Bing Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

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.

Keywords

Drug-target interactions (DTIs) CNNs Ensemble method 

Notes

Acknowledgement

This work was supported by the National Natural Science Foundation of China (Nos. 61672035, 61300058 and 61472282).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyAnhui UniversityHefeiChina
  2. 2.Institute of Physical Science and Information TechnologyAnhui UniversityHefeiChina
  3. 3.School of Electrical and Information EngineeringAnhui University of TechnologyMa’anshanChina

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