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Enhancing Chemical Safety and Toxicology Through AI
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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.
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Artificial intelligence for microplastic pollution monitoring, predictive modeling, and risk assessment: advances, challenges, and future perspectives
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This review paper explains how artificial intelligence is helping scientists detect, track, and analyze microplastics faster and more accurately than older lab methods allow. This matters because better detection tools could speed up efforts to understand how these tiny plastic particles spread through our environment, food, and bodies, ultimately supporting stronger health protections and cleanup strategies.
A Systematic Review of Artificial Intelligence and Machine Learning Techniques for Microplastic Detection and Analysis
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Scientists are increasingly using AI to detect microplastics faster than traditional lab methods like microscopes and chemical testing, according to a review of 113 studies. While AI shows promise, sometimes spotting these tiny plastic particles with over 90% accuracy, the field still lacks standardized testing methods, making it hard to compare results across studies or trust the technology is ready for widespread use. This matters because better, faster microplastic detection could eventually help us understand how much of this pollutant we're exposed to in our food, water, and air.
Environmental Toxicology: Modern Approaches for Assessing Ecological and Human Health Risks
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This review paper explains how scientists are updating the tools they use to study pollution's effects on our bodies and the environment—including newer threats like microplastics and hormone-disrupting chemicals that can build up in the food chain and cause issues like cellular stress and hormone imbalances. Instead of relying mainly on animal testing, researchers are now using lab-grown tissue models, AI, and genetic analysis to more quickly and accurately predict which chemicals are risky, helping regulators make faster, evidence-based decisions to protect public health.
Integrating Multi-Omics and Machine Learning to Predict Microplastic Cytotoxicity Under Strict Leave-One-Polymer-Out Validation
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Scientists used AI to predict how toxic different types of microplastics are to human lung cells, based on their chemical makeup. This matters because not all plastics are equally harmful, and this approach could help researchers flag risky plastics faster, without needing to test every single one in the lab.
Decoding the microplastic crisis: Machine learning and deep learning in transforming data into ecological insight
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This review paper looks at how AI tools are helping scientists detect and track microplastics faster and more accurately than manual methods. This matters because better microplastic detection could eventually help us understand how much of this pollution ends up in our environment, food, and bodies, though the tools still need refinement before they're fully reliable.
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When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.