Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment
The combination of ML, MD, and MDS provides a powerful approach for predicting new TGR5 agonists that can be optimised for T2D treatment.
Authors
- Olalekan Ademola Kemiki (ORCID: https://orcid.org/0000-0001-6837-9326)
- Chinelo Henrietta Okonkwo (ORCID: https://orcid.org/0000-0001-5421-8054)
- Ahmed Olalekan Yusuf (ORCID: https://orcid.org/0009-0004-5735-8668)
- Ojochenemi A. Enejoh (ORCID: https://orcid.org/0000-0002-5684-2472)
- Olaitan Igbagbo Awe (ORCID: https://orcid.org/0000-0002-4257-3611)
- Florence N. Mbaoji
- Ali Moses
- Hector Nortey
Institutions
- University of Nigeria (NG)
- University of Ghana (GH)
- African Institute for Mathematical Sciences (ZA)
- Babcock University (NG)
- Noguchi Memorial Institute for Medical Research (GH)
- National Hospital Abuja (NG)
- National Primary Health Care Development Agency (NG)
- Makerere University (UG)
- Bayero University Kano (NG)
Publication Details
- Journal
- Frontiers in Chemistry
- Published
- 2025-01-09
- DOI
- https://doi.org/10.3389/fchem.2024.1503593
- Citations
- 9
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 13.54
Funders
- National Institutes of Health