Predicting and Optimizing Synergistic Drug Combinations for Breast Cancer Treatment Using Machine Learning
The study demonstrates the effectiveness of machine learning in predicting synergistic drug combinations for breast cancer. By accelerating the screening process and reducing experimental burden, the approach offers a promising tool for guiding future in vitro and in vivo validation of combination therapies.
Authors
- Kuan Tak Tan (ORCID: https://orcid.org/0000-0002-0578-7098)
- Pravin R. Kshirsagar
- Saroj S. Date (ORCID: https://orcid.org/0000-0003-3393-884X)
- Rashmi Agrawal (ORCID: https://orcid.org/0000-0003-2095-5069)
- Rituraj Jain (ORCID: https://orcid.org/0000-0002-5532-1245)
- Dhyanendra Jain
- Kamal Upreti
Institutions
- Singapore Institute of Technology (SG)
- Government Medical College (IN)
- Marwadi University (IN)
- Government Medical College (IN)
- ABES Engineering College
- Christ University (IN)
- Manav Rachna International Institute of Research and Studies (IN)
Publication Details
- Journal
- American Journal of Clinical Oncology
- Published
- 2025-08-04
- DOI
- https://doi.org/10.1097/coc.0000000000001244
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00