Improving drug repositioning with negative data labeling using large language models
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.
Authors
- Mickaël Leclercq (ORCID: https://orcid.org/0000-0001-6205-888X)
- Antoine Bodein (ORCID: https://orcid.org/0000-0002-9843-504X)
- Marie‐Pier Scott‐Boyer (ORCID: https://orcid.org/0000-0003-2626-4584)
- Milan Picard (ORCID: https://orcid.org/0000-0002-2466-2012)
- Olivier Périn (ORCID: https://orcid.org/0000-0002-2590-7974)
- Arnaud Droit (ORCID: https://orcid.org/0000-0001-7922-790X)
Institutions
- L'Oréal (France) (FR)
- Université Laval (CA)
Publication Details
- Journal
- Journal of Cheminformatics
- Published
- 2025-02-04
- DOI
- https://doi.org/10.1186/s13321-025-00962-0
- Citations
- 8
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 12.03