Artificial intelligence-driven prediction and validation of blood-brain barrier permeability and absorption, distribution, metabolism, excretion profiles in Natural Product Research Laboratory compounds
Computer-based predictions for the NPRL of CMUH compounds regarding their capacity to traverse the BBB are verified by the findings. Artificial intelligence (AI) prediction models have effectively identified the potential ADME characteristics of various compounds.
Authors
- Chao‐Jung Chen (ORCID: https://orcid.org/0000-0003-3834-9243)
- Ken Ying-Kai Liao (ORCID: https://orcid.org/0000-0001-7815-8199)
- Yu-Jen Chiu (ORCID: https://orcid.org/0009-0000-5617-7102)
- Jai‐Sing Yang (ORCID: https://orcid.org/0000-0001-7302-8248)
- Kuan‐Wen Chen (ORCID: https://orcid.org/0000-0003-4325-8805)
- Shih-Chang Tsai
- DA-TIAN BAU
- Fuu‐Jen Tsai (ORCID: https://orcid.org/0000-0002-1373-245X)
- E.P. Huang
- T.-W. Liu
Institutions
- National Yang Ming Chiao Tung University (TW)
- China Medical University (TW)
- Taipei Veterans General Hospital (TW)
- China Medical University Hospital (TW)
Publication Details
- Journal
- Biomedicine
- Published
- 2024-12-01
- DOI
- https://doi.org/10.37796/2211-8039.1474
- Citations
- 3
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.08
Funders
- Office of Research and Development
- China Medical University Hospital
- China Medical University
- Division of Materials Research