Abstract
The binding of drug compounds with their targets affects the physiological functions and metabolic effects of the body, as well as the pharmacological effects leading to phenotypic effects. Studying drug-target interactions can facilitate the evaluation or identification of new targets for existing drugs. In order to better extract drug-target features and further improve the progress of model prediction, this paper investigates and analyzes a variety of graphical neural network models to predict drug-target interaction. In these models, compounds are represented as two-dimensional molecular graphs and SMILES sequences are algorithmically encoded as graph structures, which can better learn the characteristics of compound molecules. Three feature extraction strategies are experimented on the basis of two loss functions. The experimental results show that GROAN has the best results with binary cross-entropy. Compared to other methods, GROAN-based prediction model achieves better results on the benchmark dataset.
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Acknowledgment
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 in part by Hubei Province Natural Science Foundation of China (No. 2019CFB797), by National Natural Science Foundation of China (No. 61972299, 61702385).
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Li, A., Lin, X., Xu, M., Yu, H. (2021). Drug-Target Interactions Prediction with Feature Extraction Strategy Based on Graph Neural Network. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Premaratne, P. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12838. Springer, Cham. https://doi.org/10.1007/978-3-030-84532-2_50
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