AI4Cosmetics

I built the QSAR Cosmetics Skill for Claude & Claude Science to accelerate the safety assessment of cosmetic ingredients. The solution combines a curated toxicology database of nearly 5,000 chemical ingredients with an ontology linking adverse effects, tissues, organs, and biological systems, and integrates over 80 machine learning and deep learning toxicity predictions into a probabilistic Bayesian framework. The skill enables toxicology teams to rapidly screen and prioritise ingredients, generate evidence-based risk reports, identify similar compounds when data are unavailable, and support early decision-making during product development. The workflow and functionality were designed based on interviews with industry toxicologists to address real-world R&D needs. Early feedback from toxicologists in industry was highly positive. They highlighted the value of the Bayesian integration approach, the transparent data provenance provided by Claude Science, and the potential to support organisations without dedicated in-house toxicology expertise. Beyond cosmetics, reviewers also identified broader applications in chemical safety assessment. This work matters because it can significantly reduce the time required for early safety screening, helping R&D teams prioritise experimental resources, identify potential safety concerns sooner, and make more informed decisions while maintaining transparency and scientific traceability.