AI-driven identification of Zika virus inhibitors.
- DOI
- 10.1016/j.slasd.2026.100328
- Published
- 2026 Oct
- Container
- SLAS discovery : advancing life sciences R & D
- Publisher
- Not recorded
- Open access
- unknown
Credibility signals
limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1016/j.slasd.2026.100328,
title = {AI-driven identification of Zika virus inhibitors.},
author = {Otsuka Y and Shumate J and Rye TF and Goldberg I and Clement A and Scampavia L and Chung D and Spicer TP},
year = {2026},
journal = {SLAS discovery : advancing life sciences R \& D},
doi = {10.1016/j.slasd.2026.100328},
url = {https://doi.org/10.1016/j.slasd.2026.100328}
}RIS
TY - JOUR TI - AI-driven identification of Zika virus inhibitors. AU - Otsuka Y AU - Shumate J AU - Rye TF AU - Goldberg I AU - Clement A AU - Scampavia L AU - Chung D AU - Spicer TP PY - 2026 JO - SLAS discovery : advancing life sciences R & D DO - 10.1016/j.slasd.2026.100328 UR - https://doi.org/10.1016/j.slasd.2026.100328 ER -
APA
Y, O., J, S., TF, R., I, G., A, C., L, S., D, C., & TP, S. (2026). AI-driven identification of Zika virus inhibitors.. SLAS discovery : advancing life sciences R & D. https://doi.org/10.1016/j.slasd.2026.100328
Source records
- pubmed · retrieved 2026-09-25T19:25:22.038Z