Designing novel linagliptin analogs through combined generative artificial intelligence, molecular docking, molecular dynamics simulation, and binding energy estimation for targeting fibroblast activation protein
- DOI
- 10.3389/fchem.2026.1848970
- Published
- 2026-09-09
- Container
- Frontiers in Chemistry
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/fchem.2026.1848970,
title = {Designing novel linagliptin analogs through combined generative artificial intelligence, molecular docking, molecular dynamics simulation, and binding energy estimation for targeting fibroblast activation protein},
author = {Mingsong Shi and Zhi Yang and Yuhan Yang and Junxian Chen and Chuandong He and Xiaoan Li},
year = {2026},
journal = {Frontiers in Chemistry},
doi = {10.3389/fchem.2026.1848970},
url = {https://doi.org/10.3389/fchem.2026.1848970}
}RIS
TY - JOUR TI - Designing novel linagliptin analogs through combined generative artificial intelligence, molecular docking, molecular dynamics simulation, and binding energy estimation for targeting fibroblast activation protein AU - Mingsong Shi AU - Zhi Yang AU - Yuhan Yang AU - Junxian Chen AU - Chuandong He AU - Xiaoan Li PY - 2026 JO - Frontiers in Chemistry DO - 10.3389/fchem.2026.1848970 UR - https://doi.org/10.3389/fchem.2026.1848970 ER -
APA
Shi, M., Yang, Z., Yang, Y., Chen, J., He, C., & Li, X. (2026). Designing novel linagliptin analogs through combined generative artificial intelligence, molecular docking, molecular dynamics simulation, and binding energy estimation for targeting fibroblast activation protein. Frontiers in Chemistry. https://doi.org/10.3389/fchem.2026.1848970
Source records
- crossref · retrieved 2026-09-25T15:19:38.579Z