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Predictive and mechanistic multivariate linear regression models for reaction development.
Santiago, Celine B; Guo, Jing-Yao; Sigman, Matthew S.
Afiliación
  • Santiago CB; Department of Chemistry , University of Utah , 315 South 1400 East , Salt Lake City , Utah 84112 , USA . Email: sigman@chem.utah.edu.
  • Guo JY; Department of Chemistry , University of Utah , 315 South 1400 East , Salt Lake City , Utah 84112 , USA . Email: sigman@chem.utah.edu.
  • Sigman MS; Department of Chemistry , University of Utah , 315 South 1400 East , Salt Lake City , Utah 84112 , USA . Email: sigman@chem.utah.edu.
Chem Sci ; 9(9): 2398-2412, 2018 Mar 07.
Article en En | MEDLINE | ID: mdl-29719711
ABSTRACT
Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Chem Sci Año: 2018 Tipo del documento: Article

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Chem Sci Año: 2018 Tipo del documento: Article