Transfer learning from custom-tailored virtual molecular databases to real-world organic photosensitizers for catalytic activity prediction.

Noto N, Nagano T, Fujinami M, Kojima R, Saito S

Open source

DOI
10.1038/s42004-025-01678-w
Published
2025 Oct 1
Container
Communications chemistry
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.1038/s42004-025-01678-w,
  title = {Transfer learning from custom-tailored virtual molecular databases to real-world organic photosensitizers for catalytic activity prediction.},
  author = {Noto N and Nagano T and Fujinami M and Kojima R and Saito S},
  year = {2025},
  journal = {Communications chemistry},
  doi = {10.1038/s42004-025-01678-w},
  url = {https://doi.org/10.1038/s42004-025-01678-w}
}

RIS

TY  - JOUR
TI  - Transfer learning from custom-tailored virtual molecular databases to real-world organic photosensitizers for catalytic activity prediction.
AU  - Noto N
AU  - Nagano T
AU  - Fujinami M
AU  - Kojima R
AU  - Saito S
PY  - 2025
JO  - Communications chemistry
DO  - 10.1038/s42004-025-01678-w
UR  - https://doi.org/10.1038/s42004-025-01678-w
ER  -

APA

N, N., T, N., M, F., R, K., & S, S. (2025). Transfer learning from custom-tailored virtual molecular databases to real-world organic photosensitizers for catalytic activity prediction.. Communications chemistry. https://doi.org/10.1038/s42004-025-01678-w

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