Abstract
Predicting drug-target interactions plays an important role in shortening the cycle and reducing the cost of drug development. Although many existing approaches have been successful, most of them mainly start from one-dimensional sequences and do not take full advantage of the biological relationships between the drug and the target. In this paper, the heterogeneous networks are constructed based on biological properties such as drug, target, disease, side effects and their relationships. The features of drugs and targets are automatically learned using a neural network-based topology-preserving learning model. To improve the prediction accuracy of drug-target interactions, a RandSAS optimization strategy is designed, which introduces a stochastic factor based on drug-target binding affinity and drug-drug similarity to optimize the prediction process. The experimental result shows that the prediction results are relatively good when the similarity threshold is set to 0.6. In addition, based on the constructed heterogeneous network, RandSAS strategy could improve the accuracy of drug-target prediction to a certain extent.
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Acknowledgements
The authors thank the members of Machine Learning and Artificial Intelligence Laboratory, School of Computer Science and Technology, Wuhan University of Science and Technology, for their helpful discussion within seminars. This work was supported by the National Innovation and Entrepreneurship Training Program for University Students (202110488019) and the National Natural Science Foundation of China (No. 61972299).
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Xiang, H., Li, A., Lin, X. (2022). An Optimization Method for Drug-Target Interaction Prediction Based on RandSAS Strategy. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13394. Springer, Cham. https://doi.org/10.1007/978-3-031-13829-4_47
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