Predictive and mechanistic multivariate linear regression models for reaction development

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 int...

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Detalles Bibliográficos
Autores principales: Santiago, Celine B., Guo, Jing-Yao, Sigman, Matthew S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Royal Society of Chemistry 2018
Materias:
Chemistry
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5903422/
https://www.ncbi.nlm.nih.gov/pubmed/29719711
http://dx.doi.org/10.1039/c7sc04679k
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author Santiago, Celine B.
Guo, Jing-Yao
Sigman, Matthew S.
author_facet Santiago, Celine B.
Guo, Jing-Yao
Sigman, Matthew S.
author_sort Santiago, Celine B.
collection PubMed
description 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.
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spelling pubmed-59034222018-05-01 Predictive and mechanistic multivariate linear regression models for reaction development Santiago, Celine B. Guo, Jing-Yao Sigman, Matthew S. Chem Sci Chemistry 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. Royal Society of Chemistry 2018-01-23 /pmc/articles/PMC5903422/ /pubmed/29719711 http://dx.doi.org/10.1039/c7sc04679k Text en This journal is © The Royal Society of Chemistry 2018 http://creativecommons.org/licenses/by/3.0/ This article is freely available. This article is licensed under a Creative Commons Attribution 3.0 Unported Licence (CC BY 3.0)
spellingShingle Chemistry
Santiago, Celine B.
Guo, Jing-Yao
Sigman, Matthew S.
Predictive and mechanistic multivariate linear regression models for reaction development
title Predictive and mechanistic multivariate linear regression models for reaction development
title_full Predictive and mechanistic multivariate linear regression models for reaction development
title_fullStr Predictive and mechanistic multivariate linear regression models for reaction development
title_full_unstemmed Predictive and mechanistic multivariate linear regression models for reaction development
title_short Predictive and mechanistic multivariate linear regression models for reaction development
title_sort predictive and mechanistic multivariate linear regression models for reaction development
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5903422/
https://www.ncbi.nlm.nih.gov/pubmed/29719711
http://dx.doi.org/10.1039/c7sc04679k
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