A physics-informed graph neural network to approximate docking-based binding affinity for DYRK2 in Alzheimer's drug repurposing.

Gider V, Budak C

Open source

DOI
10.1038/s41598-026-35102-7
Published
2026 Feb 11
Container
Scientific reports
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.1038/s41598-026-35102-7,
  title = {A physics-informed graph neural network to approximate docking-based binding affinity for DYRK2 in Alzheimer's drug repurposing.},
  author = {Gider V and Budak C},
  year = {2026},
  journal = {Scientific reports},
  doi = {10.1038/s41598-026-35102-7},
  url = {https://doi.org/10.1038/s41598-026-35102-7}
}

RIS

TY  - JOUR
TI  - A physics-informed graph neural network to approximate docking-based binding affinity for DYRK2 in Alzheimer's drug repurposing.
AU  - Gider V
AU  - Budak C
PY  - 2026
JO  - Scientific reports
DO  - 10.1038/s41598-026-35102-7
UR  - https://doi.org/10.1038/s41598-026-35102-7
ER  -

APA

V, G., & C, B. (2026). A physics-informed graph neural network to approximate docking-based binding affinity for DYRK2 in Alzheimer's drug repurposing.. Scientific reports. https://doi.org/10.1038/s41598-026-35102-7

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