MTDDI: a graph convolutional network framework for predicting Multi-Type Drug-Drug Interactions YueHua Feng (
[email protected] ) Northwestern Polytechnical University https://orcid.org/0000-0002-3783-1305 Shao-Wu Zhang Northwestern Polytechnical University https://orcid.org/0000-0003-1305-7447 Qing-Qing Zhang Northwestern Polytechnical University https://orcid.org/0000-0002-7931-1834 Chu-Han Zhang Northwestern Polytechnical University https://orcid.org/0000-0002-2897-3918 Jian-Yu Shi Northwestern Polytechnical University https://orcid.org/0000-0002-2303-273X Research article Keywords: Drug-drug interactions (DDIs), multi-type DDIs prediction, graph convolution network (GCN), tensor factorization, deep neural network, multiple relation prediction, similarity regularization Posted Date: April 9th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-397281/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License MTDDI: a graph convolutional network framework for predicting Multi-Type Drug-Drug Interactions Yue-Hua Feng1, Shao-Wu Zhang1*, Qing-Qing Zhang1, Chu-Han Zhang2, Jian-Yu Shi3* 1 Key Laboratory of Information Fusion Technology of Ministry of Education, School of Automation, Northwestern Polytechnical University, Xi’an, 710072, China 2 School of Software, Northwestern Polytechnical University, Xian, 710072, China 3 School of Life Sciences, Northwestern Polytechnical University, Xi’an, 710072, China * Correspondence:
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[email protected] Abstract— Although the polypharmacy has both higher therapeutic efficacy and less drug resistance in combating complex diseases, drug-drug interactions (DDIs) may trigger unexpected pharmacological effects, such as side effects, adverse reactions, or even serious toxicity. Thus, it is crucial to identify DDIs and explore its underlying mechanism (e.g., DDIs types) for polypharmacy safety.