A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis
The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with...
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| Published in | Journal of the American Chemical Society Vol. 144; no. 3; pp. 1205 - 1217 |
|---|---|
| Main Authors | , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
American Chemical Society
26.01.2022
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| Subjects | |
| Online Access | Get full text |
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| Abstract | The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure–property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce kraken, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300000 new ligands. We demonstrate the application of kraken to systematically explore the property space of organophosphorus ligands and how existing data sets in catalysis can be used to accelerate ligand selection during reaction optimization. |
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| AbstractList | The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce
, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300000 new ligands. We demonstrate the application of
to systematically explore the property space of organophosphorus ligands and how existing data sets in catalysis can be used to accelerate ligand selection during reaction optimization. The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce kraken, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300000 new ligands. We demonstrate the application of kraken to systematically explore the property space of organophosphorus ligands and how existing data sets in catalysis can be used to accelerate ligand selection during reaction optimization.The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce kraken, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300000 new ligands. We demonstrate the application of kraken to systematically explore the property space of organophosphorus ligands and how existing data sets in catalysis can be used to accelerate ligand selection during reaction optimization. The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure–property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce kraken, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300000 new ligands. We demonstrate the application of kraken to systematically explore the property space of organophosphorus ligands and how existing data sets in catalysis can be used to accelerate ligand selection during reaction optimization. The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure–property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce kraken, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300000 new ligands. We demonstrate the application of kraken to systematically explore the property space of organophosphorus ligands and how existing data sets in catalysis can be used to accelerate ligand selection during reaction optimization. |
| Author | Peters, Ellyn Gaudin, Théophile Gensch, Tobias Jorner, Kjell Sigman, Matthew S Pollice, Robert dos Passos Gomes, Gabriel Nigam, AkshatKumar Aspuru-Guzik, Alán Lindner-D’Addario, Michael Friederich, Pascal |
| AuthorAffiliation | Department of Chemistry Early Chemical Development, Pharmaceutical Sciences, R&D TU Berlin Institute of Nanotechnology Chemical Physics Theory Group, Department of Chemistry AstraZeneca Department of Computer Science Lebovic Fellow University of Toronto Vector Institute for Artificial Intelligence Canadian Institute for Advanced Research (CIFAR) IBM Research Zurich |
| AuthorAffiliation_xml | – name: Department of Computer Science – name: Vector Institute for Artificial Intelligence – name: Department of Chemistry – name: TU Berlin – name: Canadian Institute for Advanced Research (CIFAR) – name: University of Toronto – name: Early Chemical Development, Pharmaceutical Sciences, R&D – name: Lebovic Fellow – name: IBM Research Zurich – name: AstraZeneca – name: Chemical Physics Theory Group, Department of Chemistry – name: Institute of Nanotechnology |
| Author_xml | – sequence: 1 givenname: Tobias orcidid: 0000-0002-1937-0285 surname: Gensch fullname: Gensch, Tobias email: tobias.gensch@tu-berlin.de organization: TU Berlin – sequence: 2 givenname: Gabriel orcidid: 0000-0002-8235-5969 surname: dos Passos Gomes fullname: dos Passos Gomes, Gabriel organization: Vector Institute for Artificial Intelligence – sequence: 3 givenname: Pascal orcidid: 0000-0003-4465-1465 surname: Friederich fullname: Friederich, Pascal organization: Institute of Nanotechnology – sequence: 4 givenname: Ellyn surname: Peters fullname: Peters, Ellyn organization: Department of Chemistry – sequence: 5 givenname: Théophile surname: Gaudin fullname: Gaudin, Théophile organization: IBM Research Zurich – sequence: 6 givenname: Robert orcidid: 0000-0001-8836-6266 surname: Pollice fullname: Pollice, Robert organization: University of Toronto – sequence: 7 givenname: Kjell surname: Jorner fullname: Jorner, Kjell organization: AstraZeneca – sequence: 8 givenname: AkshatKumar orcidid: 0000-0002-5152-2082 surname: Nigam fullname: Nigam, AkshatKumar organization: University of Toronto – sequence: 9 givenname: Michael surname: Lindner-D’Addario fullname: Lindner-D’Addario, Michael organization: University of Toronto – sequence: 10 givenname: Matthew S orcidid: 0000-0002-5746-8830 surname: Sigman fullname: Sigman, Matthew S email: sigman@chem.utah.edu organization: Department of Chemistry – sequence: 11 givenname: Alán orcidid: 0000-0002-8277-4434 surname: Aspuru-Guzik fullname: Aspuru-Guzik, Alán email: alan@aspuru.com organization: Canadian Institute for Advanced Research (CIFAR) |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35020383$$D View this record in MEDLINE/PubMed |
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| Snippet | The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local... |
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| SubjectTerms | catalysts catalytic activity humans ligands prediction |
| Title | A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis |
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