Machine learning and molecular docking prediction of potential inhibitors against dengue virus
Overall, this integrative approach, combining machine learning, molecular docking, and dynamics simulations, highlights the strength and utility of computational tools in drug discovery. It suggests a promising pathway for the rapid identification and development of novel antiviral drugs against DENV. These <i>in silico</i> findings provide a strong foundation for future experimental validations and <i>in-vitro</i> studies aimed at fighting DENV.
Authors
- Luke S. Zondagh (ORCID: https://orcid.org/0000-0002-1158-1555)
- George W. Hanson (ORCID: https://orcid.org/0000-0001-5792-8836)
- Joseph Adams
- Olaitan Igbagbo Awe (ORCID: https://orcid.org/0000-0002-4257-3611)
- Daveson I. B. Kepgang
- Lewis Tem Bueh
- Andy Asante
- Soham A. Shirolkar
- Maureen Kisaakye
- Hem Bondarwad
Institutions
- University of Ghana (GH)
- University of Buea (CM)
- African Institute for Mathematical Sciences (ZA)
- University of Douala (CM)
- Dr. Babasaheb Ambedkar Marathwada University (IN)
- University of South Florida (US)
- Noguchi Memorial Institute for Medical Research (GH)
- Makerere University (UG)
- University of the Western Cape (ZA)
Publication Details
- Journal
- Frontiers in Chemistry
- Published
- 2024-12-24
- DOI
- https://doi.org/10.3389/fchem.2024.1510029
- Citations
- 15
- Primary Topic
- Mosquito-borne diseases and control
- Type
- article
- Field-Weighted Citation Impact
- 8.14
Funders
- National Institutes of Health