SilicoTox: interpretable machine learning for molecular hazard prioritization, with analysis of database provenance bias and benchmarking across nine defined toxicity endpoints

Rinku Choudhary, Mahati Munikoti Srikantamurthy, Shovonlal Bhowmick, Sonali Chikhale, Md Ataul Islam

Open source

DOI: 10.1080/15376516.2026.2758231
Published: 2026-11-10
Container: Toxicology Mechanisms and Methods
Publisher: Informa UK Limited
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1080/15376516.2026.2758231,
  title = {SilicoTox: interpretable machine learning for molecular hazard prioritization, with analysis of database provenance bias and benchmarking across nine defined toxicity endpoints},
  author = {Rinku Choudhary and Mahati Munikoti Srikantamurthy and Shovonlal Bhowmick and Sonali Chikhale and Md Ataul Islam},
  year = {2026},
  journal = {Toxicology Mechanisms and Methods},
  doi = {10.1080/15376516.2026.2758231},
  url = {https://doi.org/10.1080/15376516.2026.2758231}
}

RIS

TY  - JOUR
TI  - SilicoTox: interpretable machine learning for molecular hazard prioritization, with analysis of database provenance bias and benchmarking across nine defined toxicity endpoints
AU  - Rinku Choudhary
AU  - Mahati Munikoti Srikantamurthy
AU  - Shovonlal Bhowmick
AU  - Sonali Chikhale
AU  - Md Ataul Islam
PY  - 2026
JO  - Toxicology Mechanisms and Methods
DO  - 10.1080/15376516.2026.2758231
UR  - https://doi.org/10.1080/15376516.2026.2758231
ER  -

APA

Choudhary, R., Srikantamurthy, M. M., Bhowmick, S., Chikhale, S., & Islam, M. A. (2026). SilicoTox: interpretable machine learning for molecular hazard prioritization, with analysis of database provenance bias and benchmarking across nine defined toxicity endpoints. Toxicology Mechanisms and Methods. https://doi.org/10.1080/15376516.2026.2758231

Source records

crossref · retrieved 2026-11-11T00:23:35.328Z