Exploring QSPR models for predicting PUF-air partition coefficients of organic compounds with linear and nonlinear approaches.

Zhu, Tengyi; Gu, Liming; Chen, Ming; Sun, Feng

Zhu, Tengyi; Gu, Liming; Chen, Ming; Sun, Feng.

Afiliação

Zhu T; School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China. Electronic address: tyzhu@yzu.edu.cn.
Gu L; School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.
Chen M; School of Civil Engineering, Southeast University, Nanjing, 210096, China.
Sun F; School of Environmental Science and Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China.

Chemosphere ; 266: 128962, 2021 Mar.

Article em En | MEDLINE | ID: mdl-33218721

Assuntos

Compostos Orgânicos; Poliuretanos; Modelos Lineares; Redes Neurais de Computação; Relação Quantitativa Estrutura-Atividade

Palavras-chave

Artificial neural network (ANN); Multiple linear regression (MLR); PUF-air partition coefficients (K(PUF-air)); Polyurethane foam (PUF); Quantitative structure-property relationship (QSPR); Support vector machine (SVM)

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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Compostos Orgânicos / Poliuretanos Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

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