Interpretable multi-modality consensus QSAR framework: integrating machine and deep learning for enhanced multi-endpoint toxicity assessment.
- DOI
- 10.1080/15376516.2026.2643659
- Published
- 2026 Jun
- Container
- Toxicology mechanisms and methods
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1080/15376516.2026.2643659,
title = {Interpretable multi-modality consensus QSAR framework: integrating machine and deep learning for enhanced multi-endpoint toxicity assessment.},
author = {Nursyafi FS and Pramudito MA and Nur Fuadah Y and Lim KM},
year = {2026},
journal = {Toxicology mechanisms and methods},
doi = {10.1080/15376516.2026.2643659},
url = {https://doi.org/10.1080/15376516.2026.2643659}
}RIS
TY - JOUR TI - Interpretable multi-modality consensus QSAR framework: integrating machine and deep learning for enhanced multi-endpoint toxicity assessment. AU - Nursyafi FS AU - Pramudito MA AU - Nur Fuadah Y AU - Lim KM PY - 2026 JO - Toxicology mechanisms and methods DO - 10.1080/15376516.2026.2643659 UR - https://doi.org/10.1080/15376516.2026.2643659 ER -
APA
FS, N., MA, P., Y, N. F., & KM, L. (2026). Interpretable multi-modality consensus QSAR framework: integrating machine and deep learning for enhanced multi-endpoint toxicity assessment.. Toxicology mechanisms and methods. https://doi.org/10.1080/15376516.2026.2643659
Source records
- pubmed · retrieved 2026-09-25T23:27:36.663Z