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Enhancing Chemical Safety and Toxicology Through AI
Summary
This review paper explains how AI tools are making it faster and more reliable to predict whether chemicals are safe, reducing the need for animal testing while still following strict scientific standards. This matters because it could speed up how quickly we identify harmful substances—including microplastics—in our environment and everyday products, giving regulators better tools to protect public health before problems occur.
This chapter provides a chemistry-centred guide to AI in safety and toxicology. It revisits QSAR/QSPR, read-across, applicability domain and new approach methodologies (NAMs), showing how modern models—multi-task learners, graph networks, conformal predictors and federated learning—extend these concepts while staying aligned with OECD principles. Practical workflows are given for building defendable QSARs, AI-assisted read-across, and mechanistically anchored ensembles that integrate structural alerts, in vitro bioactivity, omics signatures, adverse outcome pathways and PBPK/IVIVE models. The chapter stresses the distinction between hazard and risk and demonstrates how to couple hazard predictions with exposure and toxicokinetics to produce risk curves and margins of safety. Environmental applications (fate, transport, bioaccumulation, microplastics, and effect-based monitoring) are also covered. Implementation checklists focus on documentation, uncertainty, applicability domain, fairness and change control so that AI-enabled safety assessments are transparent and audit-ready.