Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challenges
Background/ObjectivesNew computational methods, based on statistical, machine learning, and deep learning techniques using drug-related entities (e.g., genes, protein bindings, etc.), help reduce the costs of in-vitro experiments through drug-drug interaction prediction (DDIp). This review examines...
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| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Pharmacology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphar.2025.1632775/full |
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| author | Flaviu-Ioan Gheorghita Vlad-Ioan Bocanet Laszlo Barna Iantovics |
| author_facet | Flaviu-Ioan Gheorghita Vlad-Ioan Bocanet Laszlo Barna Iantovics |
| author_sort | Flaviu-Ioan Gheorghita |
| collection | DOAJ |
| description | Background/ObjectivesNew computational methods, based on statistical, machine learning, and deep learning techniques using drug-related entities (e.g., genes, protein bindings, etc.), help reduce the costs of in-vitro experiments through drug-drug interaction prediction (DDIp). This review examines recent advances in DDIp. It presents an in-depth review of the state-of-the-art studies relating to semi-supervised, supervised, self-supervised learning, and other techniques such as graph-based learning and matrix factorization methods for predicting DDIs. All possible interactions between drugs are not known, and accurately predicting interactions is even more difficult due to the complex nature of drug-drug interactions (DDI).MethodsOf the 49 papers published in Web of Science in the last 6 years, 24 papers were considered relevant based on information presented in their titles and abstracts. The included articles focus specifically on predicting DDIs using a type of machine learning algorithm. Excluded articles focused on drug discovery, drug repurposing, molecular representation, or the extraction of biomedical interactions. The methodology, results limitations, and future research directions were studied for each paper. Common challenges, limitations, and future research directions were analyzed.Results and conclusionThe main limitations are class imbalance, poor performance on new drugs, limited explainability, and the need for additional data sources. |
| format | Article |
| id | doaj-art-fcfbf11dc1244e6796dc3efff597ddab |
| institution | Kabale University |
| issn | 1663-9812 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pharmacology |
| spelling | doaj-art-fcfbf11dc1244e6796dc3efff597ddab2025-08-20T03:34:45ZengFrontiers Media S.A.Frontiers in Pharmacology1663-98122025-07-011610.3389/fphar.2025.16327751632775Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challengesFlaviu-Ioan Gheorghita0Vlad-Ioan Bocanet1Laszlo Barna Iantovics2Doctoral School of Letters, Humanities and Applied Sciences, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureş, Târgu Mureş, RomaniaDepartment of Manufacturing Engineering, Technical University of Cluj-Napoca, Cluj-Napoca, RomaniaDepartment of Electrical Engineering and Information Technology, George Emil Palade University of Medicine, Pharmacy, Science, and Technology of Târgu Mureş, Târgu Mureş, RomaniaBackground/ObjectivesNew computational methods, based on statistical, machine learning, and deep learning techniques using drug-related entities (e.g., genes, protein bindings, etc.), help reduce the costs of in-vitro experiments through drug-drug interaction prediction (DDIp). This review examines recent advances in DDIp. It presents an in-depth review of the state-of-the-art studies relating to semi-supervised, supervised, self-supervised learning, and other techniques such as graph-based learning and matrix factorization methods for predicting DDIs. All possible interactions between drugs are not known, and accurately predicting interactions is even more difficult due to the complex nature of drug-drug interactions (DDI).MethodsOf the 49 papers published in Web of Science in the last 6 years, 24 papers were considered relevant based on information presented in their titles and abstracts. The included articles focus specifically on predicting DDIs using a type of machine learning algorithm. Excluded articles focused on drug discovery, drug repurposing, molecular representation, or the extraction of biomedical interactions. The methodology, results limitations, and future research directions were studied for each paper. Common challenges, limitations, and future research directions were analyzed.Results and conclusionThe main limitations are class imbalance, poor performance on new drugs, limited explainability, and the need for additional data sources.https://www.frontiersin.org/articles/10.3389/fphar.2025.1632775/fulldrug-drug interactionadverse drug reactionsmachine learning techniqueshealthcaresemi-supervised learningsupervised learning |
| spellingShingle | Flaviu-Ioan Gheorghita Vlad-Ioan Bocanet Laszlo Barna Iantovics Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challenges Frontiers in Pharmacology drug-drug interaction adverse drug reactions machine learning techniques healthcare semi-supervised learning supervised learning |
| title | Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challenges |
| title_full | Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challenges |
| title_fullStr | Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challenges |
| title_full_unstemmed | Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challenges |
| title_short | Machine learning-based drug-drug interaction prediction: a critical review of models, limitations, and data challenges |
| title_sort | machine learning based drug drug interaction prediction a critical review of models limitations and data challenges |
| topic | drug-drug interaction adverse drug reactions machine learning techniques healthcare semi-supervised learning supervised learning |
| url | https://www.frontiersin.org/articles/10.3389/fphar.2025.1632775/full |
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