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 | |
刊名 | GREEN CHEMISTRY |
2020-06-21 | |
卷号 | 22期号:12页码:3867-3876 |
ISSN号 | 1463-9262 |
DOI | 10.1039/d0gc01122c |
英文摘要 | 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. |
资助项目 | National 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关键词 | Henrys Law Constants ; Organic-compounds ; Partition-coefficients ; Green Chemistry ; Flash-point ; Water ; Qspr |
WOS研究方向 | Chemistry ; Science & Technology - Other Topics |
语种 | 英语 |
出版者 | ROYAL SOC CHEMISTRY |
WOS记录号 | WOS:000544314300016 |
资助机构 | National 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 |
内容类型 | 期刊论文 |
源URL | [http://ir.ipe.ac.cn/handle/122111/41363] |
专题 | 中国科学院过程工程研究所 |
通讯作者 | Shen, Weifeng |
作者单位 | 1.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 |
推荐引用方式 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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