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With an increasing number of biological and medical data, computational methods such as network-based methods were proposed for ADE prediction with high efficiency and low cost. However, previous network-based methods rely on the topological information of known drug-ADE networks, and hence cannot make predictions for novel compounds without any known ADE. In this study, we introduced chemical substructures to bridge the gap between the drug-ADE network and novel compounds, and developed a novel network-based method named ADENet, which can predict potential ADEs for not only drugs within the drug-ADE network, but also novel compounds outside the network. To show the performance of ADENet, we collected drug-ADE associations from a comprehensive database named MetaADEDB and constructed a series of network-based prediction models. These models obtained high area under the receiver operating characteristic curve values ranging from 0.871 to 0.947 in 10-fold cross-validation. The best model further showed high performance in external validation, which outperformed a previous network-based and a recent deep learning-based method. Using several approved drugs as case studies, we found that 32\u201354% of the predicted ADEs can be validated by the literature, indicating the practical value of ADENet. Moreover, ADENet is freely available at our web server named NetInfer (http:\/\/lmmd.ecust.edu.cn\/netinfer). In summary, our method would provide a promising tool for ADE prediction and drug safety assessment in drug discovery and development.<\/jats:p>","DOI":"10.1093\/bib\/bbab580","type":"journal-article","created":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T12:09:59Z","timestamp":1640002199000},"source":"Crossref","is-referenced-by-count":12,"title":["ADENet: a novel network-based inference method for prediction of drug adverse events"],"prefix":"10.1093","volume":"23","author":[{"given":"Zhuohang","family":"Yu","sequence":"first","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3095-6006","authenticated-orcid":false,"given":"Zengrui","family":"Wu","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China"}]},{"given":"Weihua","family":"Li","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China"}]},{"given":"Guixia","family":"Liu","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2340-1109","authenticated-orcid":false,"given":"Yun","family":"Tang","sequence":"additional","affiliation":[{"name":"Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, 130 Meilong 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