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

Area of Science:

• Chemical synthesis
• Computational chemistry
• Reaction optimization

Background:

• Multivariate linear regression models are commonly used for reaction optimization.
• Current models offer safe predictions but lack a probability forecast for desired outcomes.
• Identifying promising chemical structures with high success probability is crucial.

Purpose of the Study:

• To present a novel, easy-to-apply workflow for predicting reaction success probability.
• To enhance the identification of promising chemical structures for synthesis.
• To improve the development of mechanistically informative correlations.

Main Methods:

• Development of a probabilistic framework for reaction outcome prediction.
• Application of the framework to differentiate predictions beyond multivariate linear regression capabilities.
• Integration of probabilistic predictions into molecular development pathways.

Main Results:

• The probabilistic framework effectively predicts the likelihood of a reaction's success.
• This method differentiates predictions that are indistinguishable by standard multivariate linear regression.
• The workflow facilitates more direct pathways for molecular design and development.

Conclusions:

• The presented probabilistic workflow accelerates successful chemical discovery.
• This protocol is anticipated to be generally applicable in chemical research.
• The method enhances the efficiency of identifying and designing successful chemical reactions.