Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing
Abstract Drug development is known to be a costly and time-consuming process, which is prone to high failure rates. Drug repurposing allows drug discovery by reusing already approved compounds. The outcomes of past clinical trials can be used to predict novel drug-disease associations by leveraging...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-85927-x |
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| author | Clémence Réda Jill-Jênn Vie Olaf Wolkenhauer |
| author_facet | Clémence Réda Jill-Jênn Vie Olaf Wolkenhauer |
| author_sort | Clémence Réda |
| collection | DOAJ |
| description | Abstract Drug development is known to be a costly and time-consuming process, which is prone to high failure rates. Drug repurposing allows drug discovery by reusing already approved compounds. The outcomes of past clinical trials can be used to predict novel drug-disease associations by leveraging drug- and disease-related similarities. To tackle this classification problem, collaborative filtering with implicit feedback (and potentially additional data on drugs and diseases) has become popular. It can handle large imbalances between negative and positive known associations and known and unknown associations. However, properly evaluating the improvement over the state of the art is challenging, as there is no consensus approach to compare models. We propose a reproducible methodology for comparing collaborative filtering-based drug repurposing. We illustrate this method by comparing 11 models from the literature on eight diverse drug repurposing datasets. Based on this benchmark, we derive guidelines to ensure a fair and comprehensive evaluation of the performance of those models. In particular, an uncontrolled bias on unknown associations might lead to severe data leakage and a misestimation of the model’s true performance. Moreover, in drug repurposing, the ability of a model to extrapolate beyond its training distribution is crucial and should also be assessed. Finally, we identified a subcategory of collaborative filtering that seems efficient and robust to distribution shifts. Benchmarks constitute an essential step towards increased reproducibility and more accessible development of competitive drug repurposing methods. |
| format | Article |
| id | doaj-art-c0d19547e82d430a97af851412c9d1e0 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-c0d19547e82d430a97af851412c9d1e02025-01-26T12:32:11ZengNature PortfolioScientific Reports2045-23222025-01-0115111810.1038/s41598-025-85927-xComprehensive evaluation of pure and hybrid collaborative filtering in drug repurposingClémence Réda0Jill-Jênn Vie1Olaf Wolkenhauer2Department of Systems Biology and Bioinformatics, University of RostockSoda Team, Inria SaclayDepartment of Systems Biology and Bioinformatics, University of RostockAbstract Drug development is known to be a costly and time-consuming process, which is prone to high failure rates. Drug repurposing allows drug discovery by reusing already approved compounds. The outcomes of past clinical trials can be used to predict novel drug-disease associations by leveraging drug- and disease-related similarities. To tackle this classification problem, collaborative filtering with implicit feedback (and potentially additional data on drugs and diseases) has become popular. It can handle large imbalances between negative and positive known associations and known and unknown associations. However, properly evaluating the improvement over the state of the art is challenging, as there is no consensus approach to compare models. We propose a reproducible methodology for comparing collaborative filtering-based drug repurposing. We illustrate this method by comparing 11 models from the literature on eight diverse drug repurposing datasets. Based on this benchmark, we derive guidelines to ensure a fair and comprehensive evaluation of the performance of those models. In particular, an uncontrolled bias on unknown associations might lead to severe data leakage and a misestimation of the model’s true performance. Moreover, in drug repurposing, the ability of a model to extrapolate beyond its training distribution is crucial and should also be assessed. Finally, we identified a subcategory of collaborative filtering that seems efficient and robust to distribution shifts. Benchmarks constitute an essential step towards increased reproducibility and more accessible development of competitive drug repurposing methods.https://doi.org/10.1038/s41598-025-85927-xDrug repositioningDrug repurposingCollaborative filteringBenchmarkMatrix factorization |
| spellingShingle | Clémence Réda Jill-Jênn Vie Olaf Wolkenhauer Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing Scientific Reports Drug repositioning Drug repurposing Collaborative filtering Benchmark Matrix factorization |
| title | Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing |
| title_full | Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing |
| title_fullStr | Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing |
| title_full_unstemmed | Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing |
| title_short | Comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing |
| title_sort | comprehensive evaluation of pure and hybrid collaborative filtering in drug repurposing |
| topic | Drug repositioning Drug repurposing Collaborative filtering Benchmark Matrix factorization |
| url | https://doi.org/10.1038/s41598-025-85927-x |
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