Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis.

ABSTRACT: Rational solvent selection remains a significant challenge in process development. Here we describe a hybrid mechanistic-machine learning approach, geared towards automated process development workflow. A library of 459 solvents was used, for which 12 conventional molecular descriptors, two reaction-specific descriptors, and additional descriptors based on screening charge density, were calculated. Gaussian process surrogate models were trained on experimental data from a Rh(CO)₂(acac)/Josiphos catalysed asymmetric hydrogenation of a chiral α-β unsaturated γ-lactam. With two simultaneous objectives - high conversion and high diastereomeric excess - the multi-objective algorithm, trained on the initial dataset of 25 solvents, has identified solvents leading to better reaction out

SUBMITTER: Amar Y 

PROVIDER: S-EPMC6625492 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature