Reaching the Full Potential of Machine Learning in Mitigating Environmental Impacts of Functional Materials

He, Ying; Liu, Guohong; Liu, Chengjun; Yan, Xiliang

doi:10.1007/s44169-022-00024-8

Cited by 1 publication

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“…Another challenge is unraveling the quantitative nanostructure (or physicochemical properties of NMs)–toxicity relationships (QNTR) in high multidimensional feature spaces. , The success of artificial intelligence (AI) in the safety assessment of small organic molecules has demonstrated their ability to identify specific toxicity features from a myriad of molecular properties. − However, examples of the applications of AI to nanotoxicology are much less popular due to the paucity of model-friendly databases and the lack of suitable nanodescriptors. , Nanodescriptors, the mathematical entities that encode important physicochemical information about NMs, should apply to all types of nanostructures and properties such as composition, size, shape, and surface chemistry. They should ideally reflect the influence of the external environment (e.g., the protein coronathe layer of proteins that naturally bind to NMs in biological fluids that modulates their biological effects) .…”

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confidence: 99%

Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

et al. 2023

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Decades of nanotoxicology research have generated extensive and diverse data sets. However, data is not equal to information. The question is how to extract critical information buried in vast data streams. Here we show that artificial intelligence (AI) and molecular simulation play key roles in transforming nanotoxicity data into critical information, i.e., constructing the quantitative nanostructure (physicochemical properties)–toxicity relationships, and elucidating the toxicity-related molecular mechanisms. For AI and molecular simulation to realize their full impacts in this mission, several obstacles must be overcome. These include the paucity of high-quality nanomaterials (NMs) and standardized nanotoxicity data, the lack of model-friendly databases, the scarcity of specific and universal nanodescriptors, and the inability to simulate NMs at realistic spatial and temporal scales. This review provides a comprehensive and representative, but not exhaustive, summary of the current capability gaps and tools required to fill these formidable gaps. Specifically, we discuss the applications of AI and molecular simulation, which can address the large-scale data challenge for nanotoxicology research. The need for model-friendly nanotoxicity databases, powerful nanodescriptors, new modeling approaches, molecular mechanism analysis, and design of the next-generation NMs are also critically discussed. Finally, we provide a perspective on future trends and challenges.

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