详细信息
Data-Driven Approach for Designing Eco-Friendly Heterocyclic Compounds for the Soil Microbiome ( SCI-EXPANDED收录 EI收录) 被引量:6
文献类型:期刊文献
英文题名:Data-Driven Approach for Designing Eco-Friendly Heterocyclic Compounds for the Soil Microbiome
作者:Chen, Bingfeng[1];Liu, Meng[1];Zhang, Zhenyan[2,3];Lv, Binghai[1];Yu, Yitian[1];Zhang, Qi[2,3];Xu, Nuohan[2,3];Yang, Zhihan[1];Lu, Tao[1];Xia, Shengjie[4];Chen, Jun[5];Qian, Haifeng[1,2]
机构:[1]Zhejiang Univ Technol, Coll Environm, Hangzhou 310032, Peoples R China;[2]Shaoxing Univ, Inst Adv Study, Shaoxing 312000, Peoples R China;[3]Shaoxing Univ, Coll Chem & Chem Engn, Shaoxing 312000, Peoples R China;[4]Zhejiang Univ Technol, Coll Chem Engn, Hangzhou 310032, Peoples R China;[5]Zhejiang Shuren Univ, Interdisciplinary Res Acad, Key Lab Pollut Exposure & Hlth Intervent Zhejiang, Hangzhou 310015, Peoples R China
年份:2025
卷号:59
期号:3
起止页码:1530
外文期刊名:ENVIRONMENTAL SCIENCE & TECHNOLOGY
收录:SCI-EXPANDED(收录号:WOS:001397841300001)、、EI(收录号:20250317689235)、Scopus(收录号:2-s2.0-85214838833)、WOS
基金:This work was financially supported by the Zhejiang Provincial Natural Science Foundation (LZ23B070001), the National Natural Science Foundation of China (22376187, 42377107, 22478349, and 42307158), the National Key Research and Development Program of China (2022YFD1700401), the China Postdoctoral Science Foundation (2023M743101), the project of the Key scientific and technological Program of Hangzhou (2023SZD0058), and the Shaoxing Basic Public Welfare Special Project (2024A13004).
语种:英文
外文关键词:heterocyclic compounds; machine learning; soilmicrobiota; ecological safety; tipping point
外文摘要:Soil microbiota plays crucial roles in maintaining the health, productivity, and nutrient cycling of terrestrial ecosystems. The persistence and prevalence of heterocyclic compounds in soil pose significant risks to soil health. However, understanding the links between heterocyclic compounds and microbial responses remains challenging due to the complexity of microbial communities and their various chemical structures. This study developed a machine-learning approach that integrates the properties of chemical structures with the diversity of soil bacteria and functions to predict the impact of heterocyclic compounds on the microbial community and improve the design of eco-friendly heterocyclic compounds. We screened the key chemical structures of heterocyclic compounds-particularly those with topological polar surface areas (<74.2 & Aring;(2) or 111.3-154.1 & Aring;(2)), carboxyl groups, and dissociation constant, which maintained high soil bacterial diversity and functions, revealing threshold effects where specific structural parameters dictated microbial responses. These eco-friendly compounds stabilize communities and increase beneficial carbon and nitrogen cycle functions. By applying these design parameters, we quantitatively assessed the eco-friendliness scores of 811 heterocyclic compounds, providing a robust foundation for guiding future applications. Our study disentangles the critical chemical structure-related properties that influence the soil microbial community and establishes a computational framework for designing eco-friendly compounds with ecological benefits from an ecological perspective.
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