Issue 30, 2022

A predictive and mechanistic statistical modelling workflow for improving decision making in organic synthesis and catalysis

Abstract

The application of multivariate linear regression models has been widely utilized as a strategy to streamline the reaction optimization process. While these tools likely provide relatively safe predictions, embedding a method for forecasting the probability of achieving the desired reaction outcome would be valuable for streamlining the identification of promising structures with the best chance of success. Herein, we present a workflow that predicts the probability that a reaction will be successful and is easy and quick to apply. We show that this probabilistic framework can effectively differentiate between predictions often indistinguishable by multivariate linear regression analysis. Moreover, these techniques can enhance the development of mechanistically informative correlations by producing more direct pathways for molecular development and design. Overall, we anticipate this protocol will be generally applicable and useful for accelerating successful chemical discovery.

Graphical abstract: A predictive and mechanistic statistical modelling workflow for improving decision making in organic synthesis and catalysis

  • This article is part of the themed collection: New Talent

Supplementary files

Article information

Article type
Paper
Submitted
09 2 2022
Accepted
29 3 2022
First published
30 3 2022

Org. Biomol. Chem., 2022,20, 6012-6018

A predictive and mechanistic statistical modelling workflow for improving decision making in organic synthesis and catalysis

I. O. Betinol and J. P. Reid, Org. Biomol. Chem., 2022, 20, 6012 DOI: 10.1039/D2OB00272H

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