Abstract
Identifying drug–target interactions (DTIs) is a critical part of the drug discovery and drug development processes. Although wet lab-based methods are still the most reliable to determine DTIs, their cost and time are unaffordable. Therefore, it is particularly important to develop an effective computational method to predict DTIs. Here, we built an end-to-end deep learning framework with the Simplified Molecular Input Linear Entry System (SMILES) and protein sequences as raw data, and introduced a graph neural network and graph attention mechanism to learn the SMILES-transformed molecular graph features. We used Word2vec to process protein sequences and extract semantic features of protein sequences combined with self-attention sequence embedding models. After each group of control experiments, we used area under the ROC curve and area under the PR curve as the main evaluation indicators, and the mean of the five-fold cross-validation as the final result. The results showed that the model shows good performance on the C. elegans and human benchmark datasets.
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Acknowledgment
This work is supported by the National Natural Science Foundation of China (No. 61972299).
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Wang, K., Hu, J., Zhang, X. (2023). Identifying Drug–Target Interactions Through a Combined Graph Attention Mechanism and Self-attention Sequence Embedding Model. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14088. Springer, Singapore. https://doi.org/10.1007/978-981-99-4749-2_21
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DOI: https://doi.org/10.1007/978-981-99-4749-2_21
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