Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts-Reference-Cited by-同舟云学术

Probing machine learning models based on high throughput experimentation data for the discovery of asymmetric hydrogenation catalysts

Published:2024 Issue:34 Volume:15 Page:13618-13630
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Kalikadien Adarsh V.¹^ORCID,Valsecchi Cecile²^ORCID,van Putten Robbert³^ORCID,Maes Tor³,Muuronen Mikko³^ORCID,Dyubankova Natalia³^ORCID,Lefort Laurent³^ORCID,Pidko Evgeny A.¹^ORCID

Affiliation:

1. Inorganic Systems Engineering, Department of Chemical Engineering, Faculty of Applied Sciences, Delft University of Technology, Van der Maasweg 9, 2629 HZ, Delft, The Netherlands

2. Discovery, Product Development and Supply, Janssen Cilag S.p.A., Viale Fulvio Testi, 280/6, 20126 Milano, Italy

3. Discovery, Product Development and Supply, Janssen Pharmaceutica N.V., Turnhoutseweg 30, 2340 Beerse, Belgium

Abstract

High-throughput experimentation and computational chemistry were used to build machine learning models for Rh-catalyzed asymmetric olefin hydrogenation, identifying numerous factors affecting the accuracy of selectivity and reactivity predictions.

Funder

Janssen Pharmaceutica

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/SC/D4SC03647F

Reference93 articles.

1. Asymmetric Catalytic Hydrogenation with an Optically Active Phosphinerhodium Complex in Homogeneous Solution

3. Enantioselective Transformations in the Synthesis of Therapeutic Agents

4. Homogeneous and heterogeneous strategies of enantioselective hydrogenation: Critical evaluation and future prospects