Applying in silico ecotoxicity prediction to extensive datasets to support safer and more sustainable discovery chemistry.
- DOI
- 10.1016/j.envres.2026.125353
- Published
- 2026 Sep 15
- Container
- Environmental research
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.envres.2026.125353,
title = {Applying in silico ecotoxicity prediction to extensive datasets to support safer and more sustainable discovery chemistry.},
author = {Nikiforou F and Selvestrel G and Söderberg E and Syrén PO and von Borries K and Zheng Z and Agalliadou A and Karakoltzidis A and Halling M and Benfenati E and Karakitsios S and Sarigiannis D},
year = {2026},
journal = {Environmental research},
doi = {10.1016/j.envres.2026.125353},
url = {https://doi.org/10.1016/j.envres.2026.125353}
}RIS
TY - JOUR TI - Applying in silico ecotoxicity prediction to extensive datasets to support safer and more sustainable discovery chemistry. AU - Nikiforou F AU - Selvestrel G AU - Söderberg E AU - Syrén PO AU - von Borries K AU - Zheng Z AU - Agalliadou A AU - Karakoltzidis A AU - Halling M AU - Benfenati E AU - Karakitsios S AU - Sarigiannis D PY - 2026 JO - Environmental research DO - 10.1016/j.envres.2026.125353 UR - https://doi.org/10.1016/j.envres.2026.125353 ER -
APA
F, N., G, S., E, S., PO, S., K, V. B., Z, Z., A, A., A, K., M, H., E, B., S, K., & D, S. (2026). Applying in silico ecotoxicity prediction to extensive datasets to support safer and more sustainable discovery chemistry.. Environmental research. https://doi.org/10.1016/j.envres.2026.125353
Source records
- pubmed · retrieved 2026-09-25T21:34:46.455Z