Impact of Phosphine Featurization Methods in Process Development

Modern high-throughput experimentation (HTE) has enabled the rapid exploration of large expanses of chemical reaction space to accelerate the development of key synthetic steps in pharmaceutical processes. However, the dimensionality of reaction parameters, the desire to use minimal starting material, and the need to thoroughly analyze reaction outcomes still require the judicious selection of which experiments to perform in which order. Therefore, the development of a capability to quantify reagent diversity and analyze reaction outcomes in HTE holistically is paramount. A method to address this goal would combine computational featurization of key reaction components with the use of multivariate linear regression modeling to correlate the reaction performance outputs. In this context, we describe a process of establishing a computational featurization platform for monodentate phosphine ligands and considerations for its implementation at GSK. We demonstrate that the choice of computational method has an impact on phosphine descriptor values, ligand selection for experiments, and the development of linear regression models of reaction outcomes.

Automated summary

Modern high-throughput experimentation is crucial for accelerating the development of key synthetic steps in pharmaceutical processes. However, the selection of experiments and the analysis of reaction outcomes require careful consideration. This article presents a method to holistically analyze reaction results and quantify reagent diversity using computational featurization and multivariate linear regression modeling.