{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T04:18:11Z","timestamp":1774671491461,"version":"3.50.1"},"reference-count":87,"publisher":"Oxford University Press (OUP)","issue":"6","license":[{"start":{"date-parts":[[2022,9,27]],"date-time":"2022-09-27T00:00:00Z","timestamp":1664236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,11,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>When a drug is administered to exert its efficacy, it will encounter multiple barriers and go through multiple interactions. Predicting the drug-related multiple interactions is critical for drug development and safety monitoring because it provides foundations for practical, safe compatibility and rational use of multiple drugs. With the progress of artificial intelligence (AI) technology, a variety of novel prediction methods for single interaction have emerged and shown great advantages compared to the traditional, expensive and time-consuming laboratory research. To promote the comprehensive and simultaneous predictions of multiple interactions, we systematically reviewed the application of AI in drug\u2013drug, drug\u2013food (excipients) and drug\u2013microbiome interactions. We began by outlining the model methods, evaluation indicators, algorithms and databases commonly used to build models for three types of drug interactions. The models based on the metabolic enzyme P450, drug similarity and drug targets have empathized among the machine learning models of drug\u2013drug interactions. In particular, we discussed the limitations of current approaches and identified potential areas for future research. It is anticipated the in-depth review will be helpful for the development of the next-generation of systematic prediction models for simultaneous multiple interactions.<\/jats:p>","DOI":"10.1093\/bib\/bbac427","type":"journal-article","created":{"date-parts":[[2022,9,28]],"date-time":"2022-09-28T08:32:02Z","timestamp":1664353922000},"source":"Crossref","is-referenced-by-count":29,"title":["Artificial intelligence-driven prediction of multiple drug interactions"],"prefix":"10.1093","volume":"23","author":[{"given":"Siqi","family":"Chen","sequence":"first","affiliation":[{"name":"College of Medical Devices, Shenyang Pharmaceutical University , 103 Wenhua Road, Shenyang 110016, China"}]},{"given":"Tiancheng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Medical Devices, Shenyang Pharmaceutical University , 103 Wenhua Road, Shenyang 110016, China"}]},{"given":"Luna","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Medical Devices, Shenyang Pharmaceutical University , 103 Wenhua Road, Shenyang 110016, China"}]},{"given":"Fei","family":"Zhai","sequence":"additional","affiliation":[{"name":"College of Medical Devices, Shenyang Pharmaceutical University , 103 Wenhua Road, Shenyang 110016, China"}]},{"given":"Xiwei","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Medical Devices, Shenyang Pharmaceutical University , 103 Wenhua Road, Shenyang 110016, China"}]},{"given":"Rongwu","family":"Xiang","sequence":"additional","affiliation":[{"name":"College of Medical Devices, Shenyang Pharmaceutical University , 103 Wenhua Road, Shenyang 110016, China"},{"name":"Liaoning Medical Big Data and Artificial Intelligence Engineering Technology Research Center , Shenyang 110016, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4799-9037","authenticated-orcid":false,"given":"Guixia","family":"Ling","sequence":"additional","affiliation":[{"name":"College of Medical Devices, Shenyang Pharmaceutical 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