Improving drug repositioning with negative data labeling using large language models
Abstract Introduction Drug repositioning offers numerous advantages, such as faster development timelines, reduced costs, and lower failure rates in drug development. Supervised machine learning is commonly used to score drug candidates but is hindered by the lack of reliable negative data—drugs tha...
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BMC
2025-02-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-025-00962-0 |
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| author | Milan Picard Mickael Leclercq Antoine Bodein Marie Pier Scott-Boyer Olivier Perin Arnaud Droit |
| author_facet | Milan Picard Mickael Leclercq Antoine Bodein Marie Pier Scott-Boyer Olivier Perin Arnaud Droit |
| author_sort | Milan Picard |
| collection | DOAJ |
| description | Abstract Introduction Drug repositioning offers numerous advantages, such as faster development timelines, reduced costs, and lower failure rates in drug development. Supervised machine learning is commonly used to score drug candidates but is hindered by the lack of reliable negative data—drugs that fail due to inefficacy or toxicity— which is difficult to access, lowering their prediction accuracy and generalization. Positive-Unlabeled (PU) learning has been used to overcome this issue by either randomly sampling unlabeled drugs or identifying probable negatives but still suffers from misclassification or oversimplified decision boundaries. Results We proposed a novel strategy using Large Language Models (GPT-4) to analyze all clinical trials on prostate cancer and systematically identify true negatives. This approach showed remarkable improvement in predictive accuracy on independent test sets with a Matthews Correlation Coefficient of 0.76 (± 0.33) compared to 0.55 (± 0.15) and 0.48 (± 0.18) for two commonly used PU learning approaches. Using our labeling strategy, we created a training set of 26 positive and 54 experimentally validated negative drugs. We then applied a machine learning ensemble to this new dataset to assess the repurposing potential of the remaining 11,043 drugs in the DrugBank database. This analysis identified 980 potential candidates for prostate cancer. A detailed review of the top 30 revealed 9 promising drugs targeting various mechanisms such as genomic instability, p53 regulation, or TMPRSS2-ERG fusion. Conclusion By expanding our negative data labeling approach to all diseases within the ClinicalTrials.gov database, our method could greatly advance supervised drug repositioning, offering a more accurate and data-driven path for discovering new treatments. |
| format | Article |
| id | doaj-art-f2b48bfe9f3942cea9a71a3a59c23c1a |
| institution | Kabale University |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-f2b48bfe9f3942cea9a71a3a59c23c1a2025-02-09T12:52:16ZengBMCJournal of Cheminformatics1758-29462025-02-0117111210.1186/s13321-025-00962-0Improving drug repositioning with negative data labeling using large language modelsMilan Picard0Mickael Leclercq1Antoine Bodein2Marie Pier Scott-Boyer3Olivier Perin4Arnaud Droit5Molecular Medicine Department, CHU de Québec Research Center, Université LavalMolecular Medicine Department, CHU de Québec Research Center, Université LavalMolecular Medicine Department, CHU de Québec Research Center, Université LavalMolecular Medicine Department, CHU de Québec Research Center, Université LavalDigital Transformation and Innovation Department, L′Oréal Advanced ResearchMolecular Medicine Department, CHU de Québec Research Center, Université LavalAbstract Introduction Drug repositioning offers numerous advantages, such as faster development timelines, reduced costs, and lower failure rates in drug development. Supervised machine learning is commonly used to score drug candidates but is hindered by the lack of reliable negative data—drugs that fail due to inefficacy or toxicity— which is difficult to access, lowering their prediction accuracy and generalization. Positive-Unlabeled (PU) learning has been used to overcome this issue by either randomly sampling unlabeled drugs or identifying probable negatives but still suffers from misclassification or oversimplified decision boundaries. Results We proposed a novel strategy using Large Language Models (GPT-4) to analyze all clinical trials on prostate cancer and systematically identify true negatives. This approach showed remarkable improvement in predictive accuracy on independent test sets with a Matthews Correlation Coefficient of 0.76 (± 0.33) compared to 0.55 (± 0.15) and 0.48 (± 0.18) for two commonly used PU learning approaches. Using our labeling strategy, we created a training set of 26 positive and 54 experimentally validated negative drugs. We then applied a machine learning ensemble to this new dataset to assess the repurposing potential of the remaining 11,043 drugs in the DrugBank database. This analysis identified 980 potential candidates for prostate cancer. A detailed review of the top 30 revealed 9 promising drugs targeting various mechanisms such as genomic instability, p53 regulation, or TMPRSS2-ERG fusion. Conclusion By expanding our negative data labeling approach to all diseases within the ClinicalTrials.gov database, our method could greatly advance supervised drug repositioning, offering a more accurate and data-driven path for discovering new treatments.https://doi.org/10.1186/s13321-025-00962-0AI-driven drug discoveryComputational drug scoringNegative data labelingDrug repurposingCastration resistant prostate cancerBiomedical text mining |
| spellingShingle | Milan Picard Mickael Leclercq Antoine Bodein Marie Pier Scott-Boyer Olivier Perin Arnaud Droit Improving drug repositioning with negative data labeling using large language models Journal of Cheminformatics AI-driven drug discovery Computational drug scoring Negative data labeling Drug repurposing Castration resistant prostate cancer Biomedical text mining |
| title | Improving drug repositioning with negative data labeling using large language models |
| title_full | Improving drug repositioning with negative data labeling using large language models |
| title_fullStr | Improving drug repositioning with negative data labeling using large language models |
| title_full_unstemmed | Improving drug repositioning with negative data labeling using large language models |
| title_short | Improving drug repositioning with negative data labeling using large language models |
| title_sort | improving drug repositioning with negative data labeling using large language models |
| topic | AI-driven drug discovery Computational drug scoring Negative data labeling Drug repurposing Castration resistant prostate cancer Biomedical text mining |
| url | https://doi.org/10.1186/s13321-025-00962-0 |
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