Machine Learning–Enhanced Quantitative Structure-Activity Relationship Modeling for DNA Polymerase Inhibitor Discovery: Algorithm Development and Validation
Integrating ML with QSAR modeling provides a robust framework for optimizing hpol η inhibition, offering both high predictive accuracy and biochemical interpretability. This approach accelerates the identification of potent selective inhibitors and represents a promising strategy for overcoming cisplatin resistance, thereby advancing precision oncology.
Authors
- Robert J. Shmookler Reis (ORCID: https://orcid.org/0000-0002-4691-0734)
- Aayire C. Yadem (ORCID: https://orcid.org/0000-0002-4923-7792)
- K. Thomas Abraham (ORCID: https://orcid.org/0009-0002-9269-2536)
- Srinivas Ayyadevara (ORCID: https://orcid.org/0000-0002-4572-6361)
- César M. Compadre (ORCID: https://orcid.org/0000-0002-5652-0203)
- Samuel Kakraba (ORCID: https://orcid.org/0000-0002-6362-5126)
Institutions
- Tulane University (US)
- Shelby County Schools (US)
- Arkansas Department of Agriculture (US)
- University of Arkansas for Medical Sciences (US)
Publication Details
- Journal
- JMIR AI
- Published
- 2025-08-16
- DOI
- https://doi.org/10.2196/77890
- Citations
- 2
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 3.01