An interpretable machine learning framework for adverse drug reaction prediction from drug-target interactions
The interpretable ML framework links drug-target interactions to ADRs, offering a promising approach for predictive pharmacovigilance (PPV) and supporting safer drug development.
Authors
- Aaron Jones (ORCID: https://orcid.org/0000-0002-6282-3614)
- Marco Castellani (ORCID: https://orcid.org/0000-0002-5623-7491)
- Duc Truong Pham
- Joseph Roberts-Nuttall
Institutions
- The Edgbaston Hospital (GB)
- University of Birmingham (GB)
Publication Details
- Journal
- PLoS ONE
- Published
- 2026-01-30
- DOI
- https://doi.org/10.1371/journal.pone.0340900
- Primary Topic
- Pharmacovigilance and Adverse Drug Reactions
- Type
- article
- Field-Weighted Citation Impact
- 0.00