Details

Autor(en) / Beteiligte

• Santiago, Celine B
• Guo, Jing-Yao
• Sigman, Matthew S

Titel

Predictive and mechanistic multivariate linear regression models for reaction development

Ist Teil von

• Chemical science (Cambridge), 2018-03, Vol.9 (9), p.2398-2412

Ort / Verlag

England: Royal Society of Chemistry

Erscheinungsjahr

2018

Quelle

EZB Free E-Journals

Beschreibungen/Notizen

• 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. The utilization of physical organic molecular descriptors for the quantitative description of reaction outcomes in multivariate linear regression models is demonstrated as an effective tool for a priori prediction and mechanistic interrogation.