Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment.

Enejoh OA, Okonkwo CH, Nortey H, Kemiki OA, Moses A, Mbaoji FN, Yusuf AS, Awe OI

DOI: 10.3389/fchem.2024.1503593
Published: 2024
Container: Frontiers in chemistry
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3389/fchem.2024.1503593,
  title = {Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment.},
  author = {Enejoh OA and Okonkwo CH and Nortey H and Kemiki OA and Moses A and Mbaoji FN and Yusuf AS and Awe OI},
  year = {2024},
  journal = {Frontiers in chemistry},
  doi = {10.3389/fchem.2024.1503593},
  url = {https://doi.org/10.3389/fchem.2024.1503593}
}

RIS

TY  - JOUR
TI  - Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment.
AU  - Enejoh OA
AU  - Okonkwo CH
AU  - Nortey H
AU  - Kemiki OA
AU  - Moses A
AU  - Mbaoji FN
AU  - Yusuf AS
AU  - Awe OI
PY  - 2024
JO  - Frontiers in chemistry
DO  - 10.3389/fchem.2024.1503593
UR  - https://doi.org/10.3389/fchem.2024.1503593
ER  -

APA

OA, E., CH, O., H, N., OA, K., A, M., FN, M., AS, Y., & OI, A. (2024). Machine learning and molecular dynamics simulations predict potential TGR5 agonists for type 2 diabetes treatment.. Frontiers in chemistry. https://doi.org/10.3389/fchem.2024.1503593

Source records

pubmed · retrieved 2026-09-26T10:33:09.941Z