Open Access
| Issue |
ITM Web Conf.
Volume 87, 2026
2nd International Conference on Computing Paradigms (ICCP-2026)
|
|
|---|---|---|
| Article Number | 01014 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.1051/itmconf/20268701014 | |
| Published online | 30 June 2026 | |
- M. T. D. Cronin et al., “A Review of In Silico Toxicology Approaches to Support the Safety Assessment of Cosmetics-Related Materials,” Computational Toxicology, vol. 20, 2022. [Google Scholar]
- H. Feng et al., “Predicting the Reproductive Toxicity of Chemicals Using Ensemble Learning Methods and Molecular Fingerprints,” Toxicology Letters, vol. 340, pp. 1–10, 2021. [Google Scholar]
- S. O. Akturk, G. Tugcu, and H. Sipahi, “Development of a QSAR Model to Predict Comedogenic Potential of Cosmetic Ingredients,” Computational Toxicology, vol. 21, 2022. [Google Scholar]
- A. Di Guardo et al., “Artificial Intelligence in Cosmetic Formulation: Predictive Modeling for Safety, Tolerability, and Regulatory Perspectives,” Cosmetics, vol. 12, no. 4, 2025. [Google Scholar]
- N. Kleinstreuer et al., “AI and Machine Learning in Predictive Toxicology: Current Applications and Future Directions,” Toxicological Sciences, vol. 189, 2024. [Google Scholar]
- J. I. Bueso-Bordils et al., “Overview of Computational Toxicology Methods Applied in Machine Learning and Deep Learning,” Journal of Chemical Information and Modeling, 2024. [Google Scholar]
- S. Belfield et al., “Best Practices for QSAR Model Development and Validation in Predictive Toxicology,” PLoS ONE, vol. 18, no. 3, 2023. [Google Scholar]
- W. Kong et al., “iSKIN: Integrated Application of Machine Learning and Mondrian Conformal Prediction to Detect Skin Sensitizers in Cosmetic Raw Materials,” SmartMat, vol. 5, 2024. [Google Scholar]
- Z. Zhou et al., “Chemical Toxicity Prediction Based on Semi-Supervised Learning and Graph Convolutional Neural Networks,” Journal of Cheminformatics, vol. 13, 2021. [Google Scholar]
- S. Kim et al., “PubChem 2023 Update,” Nucleic Acids Research, vol. 51, no. D1, pp. D1373–D1380, 2023. [Google Scholar]
- J. Cremer et al., “Equivariant Graph Neural Networks for Toxicity Prediction,” ChemRxiv, 2023. [Google Scholar]
- R. Ketkar et al., “A Benchmark Study of Graph Models for Molecular Acute Toxicity,” International Journal of Molecular Sciences, vol. 24, no. 15, 2023. [Google Scholar]
- T. Hartung et al., “Artificial Intelligence as the New Frontier in Chemical Risk Assessment,” Frontiers in Artificial Intelligence, vol. 6, 2023. [Google Scholar]
- Organisation for Economic Co-operation and Development (OECD), “OECD Test No. 442D: In Vitro Skin Sensitisation,” OECD Publishing, Paris, 2022. [Google Scholar]
- D. Gadarowska et al., “Alternative Methods for Skin-Sensitization Assessment,” International Journal of Molecular Sciences, vol. 23, 2022. [Google Scholar]
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