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Converting Nanotoxicity Data to Information Using Artificial Intelligence and Simulation.
Yan, Xiliang; Yue, Tongtao; Winkler, David A; Yin, Yongguang; Zhu, Hao; Jiang, Guibin; Yan, Bing.
Afiliação
  • Yan X; Institute of Environmental Research at the Greater Bay Area, Key Laboratory for Water Quality and Conservation of the Pearl River Delta, Ministry of Education, Guangzhou University, Guangzhou 510006, China.
  • Yue T; Key Laboratory of Marine Environment and Ecology, Ministry of Education, Institute of Coastal Environmental Pollution Control, Ocean University of China, Qingdao 266100, China.
  • Winkler DA; Monash Institute of Pharmaceutical Sciences, Monash University, Parkville, Victoria 3052, Australia.
  • Yin Y; School of Pharmacy, University of Nottingham, Nottingham NG7 2QL, U.K.
  • Zhu H; Department of Biochemistry and Chemistry, La Trobe Institute for Molecular Science, La Trobe University, Melbourne, Victoria 3086, Australia.
  • Jiang G; State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
  • Yan B; Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey 08028, United States.
Chem Rev ; 123(13): 8575-8637, 2023 07 12.
Article em En | MEDLINE | ID: mdl-37262026
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Nanoestruturas Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Nanoestruturas Idioma: En Ano de publicação: 2023 Tipo de documento: Article