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Predicting the Enzymatic Hydrolysis Half-lives of New Chemicals Using Support Vector Regression Models Based on Stepwise Feature Elimination.
Shen, Wanxiang; Xiao, Tao; Chen, Shangying; Liu, Feng; Chen, Yu Zong; Jiang, Yuyang.
Afiliação
  • Shen W; Department of Chemistry, Tsinghua University, Beijing, 100084, P. R. China.
  • Xiao T; The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, P. R. China.
  • Chen S; Department of Chemistry, Tsinghua University, Beijing, 100084, P. R. China.
  • Liu F; The State Key Laboratory Breeding Base-Shenzhen Key Laboratory of Chemical Biology, the Graduate School at Shenzhen, Tsinghua University, Shenzhen, 518055, P. R. China.
  • Chen YZ; Bioinformatics and Drug Design Group, Department of Pharmacy, National University of Singapore, Singapore, 117543, Singapore.
  • Jiang Y; Department of Chemistry, Tsinghua University, Beijing, 100084, P. R. China.
Mol Inform ; 36(11)2017 11.
Article em En | MEDLINE | ID: mdl-28627805
ABSTRACT
The enzymatic hydrolysis of chemicals, which is important for in vitro drug metabolism assays, is an important indicator of drug stability profiles during drug discovery and development. Herein, we employed a stepwise feature elimination (SFE) method with nonlinear support vector machine regression (SVR) models to predict the in vitro half-lives in human plasma/blood of various esters. The SVR model was developed using public databases and literature-reported data on the half-lives of esters in human plasma/blood. In particular, the SFE method was developed to prevent over fitting and under fitting in the nonlinear model, and it provided a novel and efficient method of realizing feature combinations and selections to enhance the prediction accuracy. Our final developed model with 24 features effectively predicted an external validation set using the time-split method and presented reasonably good R2 values (0.6) and also predicted two completely independent validation datasets with R2 values of 0.62 and 0.54; thus, this model performed much better than other prediction models.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Enzimas / Máquina de Vetores de Suporte Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Mol Inform Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Enzimas / Máquina de Vetores de Suporte Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Mol Inform Ano de publicação: 2017 Tipo de documento: Article