Abstract
Drug-drug interactions (DDIs) occur when two or more drugs are taken simultaneously or successively. Early discovery of drug-drug interactions can effectively prevent medical accidents and reduce medical costs. Although several computational models have been proposed for DDI prediction, there are still limitations. These methods rely on a large amount of drug biological information and only model the interactions between drug molecules, ignoring the complex interactions between atoms. In this paper, we propose an end-to-end model based on convolutional neural network (CNN) and attention mechanism, named ACNN, to predict DDI using only drug sequence information. We use a deep CNN to learn the feature matrix for drugs. To simulate the complex interactions between atoms, we exploit the attention mechanism on the feature matrix and assign each atom an attention vector. ACNN achieves substantial performance improvement over several state-of-the-art methods for drug-drug interaction prediction. In the case study of psychiatric drugs, 7 of the 10 DDIs predicted by ACNN with the highest confidence are validated in the latest version DrugBank, further demonstrating the effectiveness of ACNN in extracting and learning drug features to predict DDI.
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Wang, W., Liu, H. (2022). ACNN: Drug-Drug Interaction Prediction Through CNN and Attention Mechanism. 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_23
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DOI: https://doi.org/10.1007/978-3-031-13829-4_23
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