Abstract
The study of drug-target interactions (DTIs) plays a crucial role in localizing new drug targets, drug discovery, and drug improvement. Nevertheless, traditional DTI experimental determination is time-consuming and labor-intensive work. Therefore, more and more researchers are investing in using computational methods to predict drug-target interactions. This paper uses the neural network model to conduct further in-depth research on DTI. We use the Word2vec model to extract protein features, use the Attentive FP model to process drugs, obtain their features, and evaluate the results through several widely used model evaluation methods. The experiment result shows that our method works well on human and C.elegans datasets.
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This work is supported by the National Natural Science Foundation of China (No. 61972299).
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Lei, Y., Hu, J., Zhao, Z., Ye, S. (2022). Drug-Target Interaction Prediction Based on Attentive FP and Word2vec. 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_44
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