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Human Health Effects
Policy & Risk
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Application of Machine Learning in Nanotoxicology: A Critical Review and Perspective
Environmental Science & Technology2024
20 citations
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Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Score: 65
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0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Iseult Lynch
Willie J.G.M. Peijnenburg,
Xiaoli Zhao,
Willie J.G.M. Peijnenburg,
Xiaoli Zhao,
Xiaoli Zhao,
Iseult Lynch
Xiaoli Zhao,
Willie J.G.M. Peijnenburg,
Zhenyu Wang,
Xiaoli Zhao,
Kmy Leung,
Xiaoli Zhao,
Martina G. Vijver,
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Xiaoli Zhao,
Yunchi Zhou,
Xiaoli Zhao,
Martina G. Vijver,
Zhenyu Wang,
Martina G. Vijver,
Willie J.G.M. Peijnenburg,
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Martina G. Vijver,
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Martina G. Vijver,
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Summary
This review evaluates how machine learning and artificial intelligence are being used to predict the toxic effects of nanomaterials, including nanoplastics, on human health and the environment. These computational tools can help screen thousands of materials for potential hazards much faster than traditional lab experiments, though the authors note that better data quality and standardized methods are still needed.
The massive production and application of nanomaterials (NMs) have raised concerns about the potential adverse effects of NMs on human health and the environment. Evaluating the adverse effects of NMs by laboratory methods is expensive, time-consuming, and often fails to keep pace with the invention of new materials. Therefore, <i>in silico</i> methods that utilize machine learning techniques to predict the toxicity potentials of NMs are a promising alternative approach if regulatory confidence in them can be enhanced. Previous reviews and regulatory OECD guidance documents have discussed in detail how to build an <i>in silico</i> predictive model for NMs. Nevertheless, there is still room for improvement in addressing the ways to enhance the model representativeness and performance from different angles, such as data set curation, descriptor selection, task type (classification/regression), algorithm choice, and model evaluation (internal and external validation, applicability domain, and mechanistic interpretation, which is key to ensuring stakeholder confidence). This review explores how to build better predictive models; the current state of the art is analyzed via a statistical evaluation of literature, while the challenges faced and future perspectives are summarized. Moreover, a recommended workflow and best practices are provided to help in developing more predictive, reliable, and interpretable models that can assist risk assessment as well as safe-by-design development of NMs.