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Predictive and mechanistic multivariate linear regression models for reaction development

期刊

CHEMICAL SCIENCE
卷 9, 期 9, 页码 2398-2412

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ROYAL SOC CHEMISTRY
DOI: 10.1039/c7sc04679k

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资金

  1. NSF [CHE-1361296]
  2. Joint Center for Energy Storage Research (JCESR) a Department of Energy, Energy Innovation Hub
  3. NIH [1 R01 GM121383]
  4. Center for High Performance Computing at the University of Utah
  5. Division Of Chemistry
  6. Direct For Mathematical & Physical Scien [1361296] Funding Source: National Science Foundation

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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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