Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation

Xiliang Yan, Tongtao Yue, David A. Winkler, Yongguang Yin, Hao Zhu, Guibin Jiang, Bing Yan

Research output: Contribution to journalReview articlepeer-review

10 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)8575-8637
Number of pages63
JournalChemical Reviews
Volume123
Issue number13
DOIs
StatePublished - Jul 12 2023

All Science Journal Classification (ASJC) codes

  • General Chemistry

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