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

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...

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Vydané v:Chemical science (Cambridge) Ročník 1; číslo 27; s. 6697 - 676
Hlavní autori: Amar, Yehia, Schweidtmann, Artur M, Deutsch, Paul, Cao, Liwei, Lapkin, Alexei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: England Royal Society of Chemistry 21.07.2019
Predmet:
Amides
Artificial intelligence
Bayesian analysis
Catalysis
Charge density
Chemistry
Conversion
Correlation coefficients
Design of experiments
Gaussian process
Genetic algorithms
Machine learning
Multiple objective analysis
Optimization
Solvents
Workflow
ISSN:2041-6520, 2041-6539
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Shrnutí: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 outcomes. In addition to being a powerful design of experiments (DoE) methodology, the resulting Gaussian process surrogate model for conversion is, in statistical terms, predictive, with a cross-validation correlation coefficient of 0.84. After identifying promising solvents, the composition of solvent mixtures and optimal reaction temperature were found using a black-box Bayesian optimisation. We then demonstrated the application of a new genetic programming approach to select an appropriate machine learning model for a specific physical system, which should allow the transition of the overall process development workflow into the future robotic laboratories. Rational solvent selection remains a significant challenge in process development.
Bibliografia:10.1039/c9sc01844a
Electronic supplementary information (ESI) available: Library of solvents and molecular descriptors. See DOI
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ISSN:2041-6520
2041-6539
DOI:10.1039/c9sc01844a