Issue 9, 2019

Making better decisions during synthetic route design: leveraging prediction to achieve greenness-by-design

Abstract

Modern pharmaceuticals are becoming increasingly complex. Incorporating knowledge of a route's holistic sustainability during the route design process could be a critical enabler to minimizing the environmental impact of pharmaceutical manufacturing. The pursuit of the optimal synthesis has historically been characterized by disconnection strategy, or things like step count, however, the optimal synthesis of a molecule may also be assessed through environmentally relevant metrics. The synthesis with the lowest possible cumulative process mass intensity (cPMI) could be considered optimal, a route which may not necessarily be the shortest, but has the best holistic sustainability (for example, considering the synthesis of all reagents and reactants). Previously, we demonstrated the importance of assessing the entire synthetic network by including “above-the-arrow” reagents/reactants into cPMI, to reflect the impact of reagents, such as ligands, on the overall sustainability of the route. Here we present the development of a machine learning approach, using substrate fingerprints, to build a multiclass predictive model to identify which ligands will likely function in a Pd-catalyzed C–N coupling reaction. The resulting predicted multiclass probabilities were then linked to the corresponding ligand cPMIs to yield a probability-weighted predicted holistic PMI for the transformation, integrating the synthesis of the ligand. This proof-of-confidence study may extend our ability to holistically assess different synthetic route options, considering their full impact, to aid decision-making during route ideation. This may lead to greener outcomes in the development of synthetic routes in the pharmaceutical sector and beyond.

Graphical abstract: Making better decisions during synthetic route design: leveraging prediction to achieve greenness-by-design

Supplementary files

Article information

Article type
Paper
Submitted
13 Janv. 2019
Accepted
19 Febr. 2019
First published
19 Febr. 2019

React. Chem. Eng., 2019,4, 1595-1607

Making better decisions during synthetic route design: leveraging prediction to achieve greenness-by-design

J. Li and M. D. Eastgate, React. Chem. Eng., 2019, 4, 1595 DOI: 10.1039/C9RE00019D

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements