Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach
The range of chemical databases available has dramatically increased in recent years, but the reliability and quality of their data are often negatively affected by human-error fidelity. The size of chemical databases can make manual data curation/checking of such sets time consuming; thus, automate...
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MDPI AG
2025-01-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/30/2/355 |
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| author | Eduardo Aguilar-Bejarano Viraj Deorukhkar Simon Woodward |
| author_facet | Eduardo Aguilar-Bejarano Viraj Deorukhkar Simon Woodward |
| author_sort | Eduardo Aguilar-Bejarano |
| collection | DOAJ |
| description | The range of chemical databases available has dramatically increased in recent years, but the reliability and quality of their data are often negatively affected by human-error fidelity. The size of chemical databases can make manual data curation/checking of such sets time consuming; thus, automated tools to help this process are highly desirable. Herein, we propose the use of Graph Neural Networks (GNNs) to identifying potential stereochemical misassignments in the primary asymmetric catalysis literature. Our method relies on the use of an ensemble of GNN models to predict the expected stereoselectivity of exemplars for a particular asymmetric reaction. When the majority of these models do not correlate to the reported outcome, the point is labeled as a possible stereochemical misassignment. Such identified cases are few in number and more easily investigated for their cause. We demonstrate the use of this approach to spot potential literature stereochemical misassignments in the ketone products resulting from catalytic asymmetric 1,4-addition of organoboron nucleophiles to Michael acceptors in two different databases, each one using a different family of chiral ligands (bisphosphine and diene ligands). Our results demonstrate that this methodology is useful for curation of medium-sized databases, speeding this process significantly compared to complete manual curation/checking. In the datasets investigated, human expert checking was reduced to 2.2% and 3.5% of the total data exemplars. |
| format | Article |
| id | doaj-art-40538181c0fb4fc08d837fcdc5863911 |
| institution | Kabale University |
| issn | 1420-3049 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj-art-40538181c0fb4fc08d837fcdc58639112025-01-24T13:43:44ZengMDPI AGMolecules1420-30492025-01-0130235510.3390/molecules30020355Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network ApproachEduardo Aguilar-Bejarano0Viraj Deorukhkar1Simon Woodward2GSK Carbon Neutral Laboratories for Sustainable Chemistry, Jubilee Campus, University of Nottingham, Triumph Road, Nottingham NG7 2TU, UKYusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, UKGSK Carbon Neutral Laboratories for Sustainable Chemistry, Jubilee Campus, University of Nottingham, Triumph Road, Nottingham NG7 2TU, UKThe range of chemical databases available has dramatically increased in recent years, but the reliability and quality of their data are often negatively affected by human-error fidelity. The size of chemical databases can make manual data curation/checking of such sets time consuming; thus, automated tools to help this process are highly desirable. Herein, we propose the use of Graph Neural Networks (GNNs) to identifying potential stereochemical misassignments in the primary asymmetric catalysis literature. Our method relies on the use of an ensemble of GNN models to predict the expected stereoselectivity of exemplars for a particular asymmetric reaction. When the majority of these models do not correlate to the reported outcome, the point is labeled as a possible stereochemical misassignment. Such identified cases are few in number and more easily investigated for their cause. We demonstrate the use of this approach to spot potential literature stereochemical misassignments in the ketone products resulting from catalytic asymmetric 1,4-addition of organoboron nucleophiles to Michael acceptors in two different databases, each one using a different family of chiral ligands (bisphosphine and diene ligands). Our results demonstrate that this methodology is useful for curation of medium-sized databases, speeding this process significantly compared to complete manual curation/checking. In the datasets investigated, human expert checking was reduced to 2.2% and 3.5% of the total data exemplars.https://www.mdpi.com/1420-3049/30/2/355graph neural networksstereochemical misassignmentdatabase curation |
| spellingShingle | Eduardo Aguilar-Bejarano Viraj Deorukhkar Simon Woodward Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach Molecules graph neural networks stereochemical misassignment database curation |
| title | Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach |
| title_full | Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach |
| title_fullStr | Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach |
| title_full_unstemmed | Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach |
| title_short | Data Checking of Asymmetric Catalysis Literature Using a Graph Neural Network Approach |
| title_sort | data checking of asymmetric catalysis literature using a graph neural network approach |
| topic | graph neural networks stereochemical misassignment database curation |
| url | https://www.mdpi.com/1420-3049/30/2/355 |
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