Machine learning and deep learning-based drug-drug interactions prediction: a systematic review focused on anticancer drugsDownload PDF Download PDF ArticleOpen accessPublished: 03 June 2026Yingying Zhao1 na1,Jiaqi Wang1 na1,Jiyeong Kim2,Fatima Rodriguez2,3,4,Eleni Linos2,5,Rong Na6,Khuloud T. Al-Jamal1,7 &…Xue Li1,2,8 npj Precision Oncology (2026) Cite this article We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.SubjectsCancerComputational biology and bioinformaticsDrug discoveryAbstractCancer patients are particularly susceptible to Drug–Drug Interactions (DDIs) due to frequent polypharmacy in oncology care. Co-administered drugs can increase toxicity or reduce effectiveness, potentially causing serious adverse events—for example, QTc-prolonging Tyrosine Kinase Inhibitors with CYP3A4 inhibitors can lead to torsade de pointes. Traditional DDI identification methods are time-consuming and costly, relying mainly on in vitro and in vivo wet lab experiments, clinical studies, or post-marketing surveillance. Many Machine Learning (ML) and Deep Learning (DL)-based DDI prediction models have been developed in recent decades to accelerate the identification of DDIs. We systematically reviewed ML- and DL-based DDI prediction models involving anticancer drugs. Key features of anticancer drugs involved and details of prediction models, such as the prediction tasks (existence or types of DDI) and performance, were summarised, as well as a list of newly predicted DDIs. Additionally, verification through up-to-date DrugBank and Drugs.com confirmed 22 of 96 newly predicted potential DDI drug pairs, demonstrating the practical value of these techniques. By understanding the current DDI prediction studies from both methodological and clinical standpoints, novel approaches may be tailored to the unique characteristics of oncology drugs, thereby enhancing the clinical relevance and applicability of DDI predictions.The alternative text for this image may have been generated using AI.AcknowledgementsThis study was supported by the HKUMed Research Collaboration Booster Fund from The University of Hong Kong.Author informationAuthor notesThese authors contributed equally: Yingying Zhao, Jiaqi Wang.Authors and AffiliationsDepartment of Pharmacology and Pharmacy, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, ChinaYingying Zhao, Jiaqi Wang, Khuloud T. Al-Jamal & Xue LiStanford Center for Digital Health, Stanford University, Stanford, CA, USAJiyeong Kim, Fatima Rodriguez, Eleni Linos & Xue LiDivision of Cardiovascular Medicine, Department of Medicine, Stanford University, Stanford, CA, USAFatima RodriguezStanford Cardiovascular Institute, Stanford University, Stanford, CA, USAFatima RodriguezDepartment of Dermatology, Stanford University, Stanford, CA, USAEleni LinosDepartment of Surgery, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, ChinaRong NaInstitute of Pharmaceutical Science, Faculty of Life Sciences & Medicine, King’s College London, London, SE1 9NH, UKKhuloud T. Al-JamalDepartment of Medicine, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong SAR, ChinaXue LiAuthorsYingying ZhaoView author publicationsSearch author on:PubMed Google ScholarJiaqi WangView author publicationsSearch author on:PubMed Google ScholarJiyeong KimView author publicationsSearch author on:PubMed Google ScholarFatima RodriguezView author publicationsSearch author on:PubMed Google ScholarEleni LinosView author publicationsSearch author on:PubMed Google ScholarRong NaView author publicationsSearch author on:PubMed Google ScholarKhuloud T. Al-JamalView author publicationsSearch author on:PubMed Google ScholarXue LiView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to Xue Li.Ethics declarationsCompeting interestsF.R. reports consulting fees from Novartis, Esperion Therapeutics, Edwards, Arrowhead Pharmaceuticals, HeartFlow, iRhythm, Amgen, and Cleerly Health outside the submitted work. X.L. received research grants or contracts from the Health and Medical Research Fund (HMRF) Main Scheme and HMRF Fellowship Scheme of the Hong Kong Special Administrative Region(SAR), China, and from the Research Grants Council Early Career Scheme, Research Impact Fund (Hong Kong SAR); is also the former nonexecutive director of ADAMS Hong Kong SAR; received commission grants from Department of Health, Government of Hong Kong SAR, China; commission grants from Hospital Authority of Hong Kong SAR, China, internal funding from the University of Hong Kong, Hong Kong SAR, China, and research or education grants from Pfizer, Janssen, Bristol Myers Squibb (BMS) and Novartis; received consultancy fees from Merck Sharp & Dohme, Pfizer, Open Health and The Office of Health Economics ; and received honoraria for associate editorship from Springer Nature. 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