Author

José Ferraz-Caetano

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Open access Sep 2026

Machine Learning for Go/No-Go Screening of Acute Dermal Toxicity to Support Sustainable Chemical Design

An XGBoost Machine Learning (ML) classifier to support Go/No-Go decisions on the acute dermal toxicity of small organic molecules used in materials design and indicates progression to a next screening stage, which is needed to prove that a chemical is sustainable.

José Ferraz-Caetano, António Nogueira, Juliana Rodrigues et al. · ChemEngineering · 0 citations

#explainable ai Sep 2026

The Role of Knowledge Representation & Reasoning in Deciphering Chemical Complexity

Modern chemistry is pushing the limits of traditional Artificial Intelligence (AI) models, placing unprecedented demands on data availability to address humanity's most pressing challenges. One particular concern is AI's dependence on large, curated data and its tendency to deviate from or misrepresent fundamental chem...

José Ferraz-Caetano, Filipe Teixeira, M. N. D. S. Cordeiro · WIREs Computational Molecula... · 0 citations