In Silico Regression Modeling and Improved Interpretability To Predict the Transport Inhibitory Activity of Breast Cancer Resistance Protein
value of 0.703 on the test set. Based on SHapley Additive exPlanations (SHAP) values, several substructures were identified as key features by the finalized model, and the prediction results revealed seven potential BCRP inhibitors among approved drugs. These models contribute to accelerating compound screening and reducing drug development costs, while also aiding in the exclusion of potential dropout candidates.
Authors
- Reiko Watanabe (ORCID: https://orcid.org/0000-0001-9359-8731)
- Donny Ramadhan (ORCID: https://orcid.org/0000-0001-6556-3160)
- Kenji Mizuguchi (ORCID: https://orcid.org/0000-0003-3021-7078)
- Kaoru Takadera
Institutions
- National Institute of Biomedical Innovation, Health and Nutrition (JP)
- Protein Research Foundation (JP)
- Artificial Intelligence in Medicine (Canada) (CA)
- Chemical Pharmaceutical Research Institute (BG)
- National Research and Innovation Agency (ID)
- Graduate School USA (US)
- Kumamoto University (JP)
Publication Details
- Journal
- ACS Omega
- Published
- 2026-04-21
- DOI
- https://doi.org/10.1021/acsomega.5c12191
- Primary Topic
- Machine Learning in Bioinformatics
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Osaka University
- Lembaga Pengelola Dana Pendidikan