Abstract
Adverse Drug-Drug Interactions(DDI) occur with drug combinations and mainly cause mortality and morbidity. The identification of potential DDI is essential for medical health. Most of the existing methods rely heavily on manually engineered domain knowledge, therefore, lack generalization and are inefficient. Drugs with similar molecular structures have similar chemical properties. The molecular structures of drugs can be obtained easily and the reactions between drugs are reactions between two molecular structures. In this paper, we proposed an artificially intelligent DDI prediction model, Molecular Structure-based Double-Central Drug-Drug Interaction prediction(MSDC-DDI). MSDC-DDI utilizes a double-central encoder and a cross-dependent schema to generate the representations of the drugs. MSDC-DDI made effective and accurate predictions, which achieved up to more than 99% in DDI prediction.
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Baitai, C., Peng, J., Zhang, Y., Liu, Y. (2023). Molecular Structure-Based Double-Central Drug-Drug Interaction Prediction. In: Iliadis, L., Papaleonidas, A., Angelov, P., Jayne, C. (eds) Artificial Neural Networks and Machine Learning – ICANN 2023. ICANN 2023. Lecture Notes in Computer Science, vol 14257. Springer, Cham. https://doi.org/10.1007/978-3-031-44216-2_11
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