DrugCNN: A Convolutional Neural Network System to

Discriminate Drug-Target Interactions


  Biological targets are most commonly proteins such as enzymes, ion channels, and receptors. They are anything within a living organism to bind with some other entities (like an endogenous ligand or a drug), resulting in change in their behaviors or functions. Exploring potential drug-target interactions (DTIs) are crucial for drug discovery and effective drug development. Computational methods were widely applied in drug-target interactions, since experimental methods are extremely time-consuming and resource-intensive. In this paper, we proposed a novel deep learning-based prediction system, with a new negative instance generation, to identify DTIs. As a result, our method achieved an accuracy of 0.9800 on our created dataset. Another dataset derived from DrugBank was used to further assess the generalization of the model, which yielded a good performance with accuracy of 0.8814 and AUC value of 0.9527 on the dataset. The outcome of our experimental results indicated that the proposed method, involving the credible negative generation, can be employed to discriminate the interactions between drugs and targets.


Supplementary files:

drug-target datasets and supplementary files: supplementary (including enzymes, icon channels, GPCRs and nuclear receptors).


ShanShan Hu, DeNan Xia, Benyue Su, Peng Chen, Bing Wang, Jinyan Li, A Convolutional Neural Network System to Discriminate Drug-Target Interactions, IEEE/ACM Transactions on Computational Biology and Bioinformatics, in revision.

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