Artificial Intelligence and Machine Learning Models for Predicting Drug-Induced Kidney Injury in Small Molecules
The integrated model, which combines both physicochemical properties and off-target interaction data, significantly improved DIKI prediction accuracy compared to models that rely on either data type alone. This AI/ML model provides a promising early screening tool for identifying compounds with lower DIKI risk, facilitating safer drug development.
Authors
- Amy H. Yang
- Satjit Brar
- Mohan Rao (ORCID: https://orcid.org/0000-0001-7395-5260)
- Vahid Nassiri (ORCID: https://orcid.org/0000-0003-4663-3930)
- Clifford Sachs
- Eric McDuffie
- Sanjay Srivastava (ORCID: https://orcid.org/0000-0001-6535-0712)
Institutions
- Neurocrine Biosciences (United States) (US)
Publication Details
- Journal
- Pharmaceuticals
- Published
- 2024-11-19
- DOI
- https://doi.org/10.3390/ph17111550
- Citations
- 9
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 3.25