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A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties
Wang, Zihao1; Su, Yang1; Jin, Saimeng1; Shen, Weifeng1; Ren, Jingzheng2; Zhang, Xiangping3; Clark, James H.4
2020-06-21
Source PublicationGREEN CHEMISTRY
ISSN1463-9262
Volume22Issue:12Pages:3867-3876
Abstract

Environmental properties of compounds provide significant information in treating organic pollutants, which drives the chemical process and environmental science toward eco-friendly technology. Traditional group contribution methods play an important role in property estimations, whereas various disadvantages emerge in their applications, such as scattered predicted values for certain groups of compounds. In order to address such issues, an extraction strategy for molecular features is proposed in this research, which is characterized by interpretability and discriminating power with regard to isomers. Based on the Henry's law constant data of organic compounds in water, we developed a hybrid predictive model that integrates the proposed strategy in conjunction with a neural network framework. The structure of the predictive model is optimized using cross-validation and grid search to improve its robustness. Moreover, the predictive model is improved by introducing the plane of best fit descriptor as input and adopting k-means clustering in sampling. In contrast with reported models in the literature, the developed predictive model demonstrates improved generality, higher accuracy, and fewer molecular features used in its development.

DOI10.1039/d0gc01122c
Language英语
WOS KeywordHenrys Law Constants ; Organic-compounds ; Partition-coefficients ; Green Chemistry ; Flash-point ; Water ; Qspr
Funding ProjectNational Natural Science Foundation of China[21878028] ; Fundamental Research Funds for the Central Universities[2019CDQYHG021] ; Chongqing Innovation Support Program for Returned Overseas Chinese Scholars[CX2018048] ; Beijing Hundreds of Leading Talents Training Project of Science and Technology[Z171100001117154]
WOS Research AreaChemistry ; Science & Technology - Other Topics
WOS SubjectChemistry, Multidisciplinary ; Green & Sustainable Science & Technology
Funding OrganizationNational Natural Science Foundation of China ; Fundamental Research Funds for the Central Universities ; Chongqing Innovation Support Program for Returned Overseas Chinese Scholars ; Beijing Hundreds of Leading Talents Training Project of Science and Technology
WOS IDWOS:000544314300016
PublisherROYAL SOC CHEMISTRY
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Document Type期刊论文
Identifierhttp://ir.ipe.ac.cn/handle/122111/41364
Collection中国科学院过程工程研究所
Corresponding AuthorShen, Weifeng
Affiliation1.Chongqing Univ, Sch Chem & Chem Engn, Chongqing 400044, Peoples R China
2.Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China
3.Chinese Acad Sci, Inst Proc Engn, Beijing Key Lab Ion Liquids Clean Proc, CAS Key Lab Green Proc & Engn, Beijing 100190, Peoples R China
4.Univ York, Green Chem Ctr Excellence, York YO10 5DD, N Yorkshire, England
Recommended Citation
GB/T 7714
Wang, Zihao,Su, Yang,Jin, Saimeng,et al. A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties[J]. GREEN CHEMISTRY,2020,22(12):3867-3876.
APA Wang, Zihao.,Su, Yang.,Jin, Saimeng.,Shen, Weifeng.,Ren, Jingzheng.,...&Clark, James H..(2020).A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties.GREEN CHEMISTRY,22(12),3867-3876.
MLA Wang, Zihao,et al."A novel unambiguous strategy of molecular feature extraction in machine learning assisted predictive models for environmental properties".GREEN CHEMISTRY 22.12(2020):3867-3876.
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