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Predictive deep learning models for environmental properties: the direct calculation of octanol-water partition coefficients from molecular graphs
Wang, Zihao1; Su, Yang1; Shen, Weifeng1; Jin, Saimeng1; Clark, James H.2; Ren, Jingzheng3; Zhang, Xiangping4
2019-08-21
Source PublicationGREEN CHEMISTRY
ISSN1463-9262
Volume21Issue:16Pages:4555-4565
AbstractAs an essential environmental property, the octanol-water partition coefficient (K-OW) quantifies the lipophilicity of a compound and it could be further employed to predict toxicity. Thus, it is an indispensable factor that should be considered for screening and development of green solvents with respect to unconventional and novel compounds. Herein, a deep-learning-assisted predictive model has been developed to accurately and reliably calculate log K-OW values for organic compounds. An embedding algorithm was specifically established for generating signatures automatically for molecular structures to express structural information and connectivity. Afterwards, the Tree-structured long short-term memory (Tree-LSTM) network was used in conjunction with signature descriptors for automatic feature selection, and it was then coupled with the back-propagation neural network to develop a deep neural network (DNN), which is used for modeling quantity structure-property relationship (QSPR) to predict log K-OW. Compared with an authoritative estimation method, the proposed DNN-based QSPR model exhibited better predictive accuracy and greater discriminative power in terms of the structural isomers and stereoisomers. As such, the proposed deep learning approach can act as a promising and intelligent tool for developing environmental property prediction methods for guiding development or screening of green solvents.
DOI10.1039/c9gc01968e
Language英语
WOS KeywordPHYSICOCHEMICAL PROPERTIES ; EXTRACTIVE DISTILLATION ; IONIC LIQUIDS ; AQUEOUS SOLUBILITY ; GREEN CHEMISTRY ; QSAR MODELS ; PART 2. ; CHEMICALS ; DESIGN ; VALIDATION
Funding ProjectNational Natural Science Foundation of China[21606026] ; National Natural Science Foundation of China[21878028] ; Fundamental Research Funds for the Central Universities[2019CDQYHG021] ; Fundamental Research Funds for the Central Universities[2019CDXYHG0013] ; 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 ; Beijing Hundreds of Leading Talents Training Project of Science and Technology
WOS IDWOS:000480643800028
PublisherROYAL SOC CHEMISTRY
Citation statistics
Cited Times:26[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ipe.ac.cn/handle/122111/30537
Collection中国科学院过程工程研究所
Corresponding AuthorShen, Weifeng; Jin, Saimeng
Affiliation1.Chongqing Univ, Sch Chem & Chem Engn, Chongqing 400044, Peoples R China
2.Univ York, Green Chem Ctr Excellence, York YO105D, N Yorkshire, England
3.Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hong Kong, Peoples R China
4.Chinese Acad Sci, Beijing Key Lab Ion Liquids Clean Proc, Inst Proc Engn, CAS Key Lab Green Proc & Engn, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Wang, Zihao,Su, Yang,Shen, Weifeng,et al. Predictive deep learning models for environmental properties: the direct calculation of octanol-water partition coefficients from molecular graphs[J]. GREEN CHEMISTRY,2019,21(16):4555-4565.
APA Wang, Zihao.,Su, Yang.,Shen, Weifeng.,Jin, Saimeng.,Clark, James H..,...&Zhang, Xiangping.(2019).Predictive deep learning models for environmental properties: the direct calculation of octanol-water partition coefficients from molecular graphs.GREEN CHEMISTRY,21(16),4555-4565.
MLA Wang, Zihao,et al."Predictive deep learning models for environmental properties: the direct calculation of octanol-water partition coefficients from molecular graphs".GREEN CHEMISTRY 21.16(2019):4555-4565.
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