Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis
Summary: Optimization of metal-ligand asymmetric catalysts is usually done by empirical trials, where the ligand is arbitrarily modified, and the new catalyst is re-evaluated in the lab. This procedure is not efficient and alternative strategies are highly desirable. We propose the Homogeneous Catal...
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Elsevier
2025-03-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004225001415 |
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| author | Eduardo Aguilar-Bejarano Ender Özcan Raja K. Rit Hongyi Li Hon Wai Lam Jonathan C. Moore Simon Woodward Grazziela Figueredo |
| author_facet | Eduardo Aguilar-Bejarano Ender Özcan Raja K. Rit Hongyi Li Hon Wai Lam Jonathan C. Moore Simon Woodward Grazziela Figueredo |
| author_sort | Eduardo Aguilar-Bejarano |
| collection | DOAJ |
| description | Summary: Optimization of metal-ligand asymmetric catalysts is usually done by empirical trials, where the ligand is arbitrarily modified, and the new catalyst is re-evaluated in the lab. This procedure is not efficient and alternative strategies are highly desirable. We propose the Homogeneous Catalyst Graph Neural Network (HCat-GNet), a machine learning model capable of aiding ligand optimization. This method trains models to predict the enantioselectivity of asymmetric reactions using only the SMILES representations of the participant molecules. HCat-GNet allows high interpretability indicating from which atoms the model gathers the most predictive information, thus showing which atoms within the ligand most affect the increase or decrease in the reaction’s selectivity. The validation of the model’s selectivity predictions is made using a new class of ligand for rhodium-catalyzed asymmetric 1,4-addition, demonstrating the ability of HCat-GNet to extrapolate into unknown chiral ligand space. Validation with other benchmark asymmetric reaction datasets demonstrates its generality when modeling different reactions. |
| format | Article |
| id | doaj-art-78b63a978cb944518697b7d151a8d84e |
| institution | OA Journals |
| issn | 2589-0042 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj-art-78b63a978cb944518697b7d151a8d84e2025-08-20T02:15:24ZengElsevieriScience2589-00422025-03-0128311188110.1016/j.isci.2025.111881Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysisEduardo Aguilar-Bejarano0Ender Özcan1Raja K. Rit2Hongyi Li3Hon Wai Lam4Jonathan C. Moore5Simon Woodward6Grazziela Figueredo7GSK Carbon Neutral Laboratories for Sustainable Chemistry, University of Nottingham, Jubilee Campus, Triumph Road, Nottingham NG7 2TU, UK; School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK; School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, Nottingham NG8 1BB, UKSchool of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham, Nottingham NG8 1BB, UKGSK Carbon Neutral Laboratories for Sustainable Chemistry, University of Nottingham, Jubilee Campus, Triumph Road, Nottingham NG7 2TU, UK; School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UKGSK Carbon Neutral Laboratories for Sustainable Chemistry, University of Nottingham, Jubilee Campus, Triumph Road, Nottingham NG7 2TU, UK; School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UKGSK Carbon Neutral Laboratories for Sustainable Chemistry, University of Nottingham, Jubilee Campus, Triumph Road, Nottingham NG7 2TU, UK; School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UKGSK Carbon Neutral Laboratories for Sustainable Chemistry, University of Nottingham, Jubilee Campus, Triumph Road, Nottingham NG7 2TU, UK; School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UKGSK Carbon Neutral Laboratories for Sustainable Chemistry, University of Nottingham, Jubilee Campus, Triumph Road, Nottingham NG7 2TU, UK; School of Chemistry, University of Nottingham, University Park, Nottingham NG7 2RD, UK; Corresponding authorSchool of Medicine, University of Nottingham, Medical School, Nottingham NG7 2UH, UK; Corresponding authorSummary: Optimization of metal-ligand asymmetric catalysts is usually done by empirical trials, where the ligand is arbitrarily modified, and the new catalyst is re-evaluated in the lab. This procedure is not efficient and alternative strategies are highly desirable. We propose the Homogeneous Catalyst Graph Neural Network (HCat-GNet), a machine learning model capable of aiding ligand optimization. This method trains models to predict the enantioselectivity of asymmetric reactions using only the SMILES representations of the participant molecules. HCat-GNet allows high interpretability indicating from which atoms the model gathers the most predictive information, thus showing which atoms within the ligand most affect the increase or decrease in the reaction’s selectivity. The validation of the model’s selectivity predictions is made using a new class of ligand for rhodium-catalyzed asymmetric 1,4-addition, demonstrating the ability of HCat-GNet to extrapolate into unknown chiral ligand space. Validation with other benchmark asymmetric reaction datasets demonstrates its generality when modeling different reactions.http://www.sciencedirect.com/science/article/pii/S2589004225001415Artificial intelligenceCatalysisChemistry |
| spellingShingle | Eduardo Aguilar-Bejarano Ender Özcan Raja K. Rit Hongyi Li Hon Wai Lam Jonathan C. Moore Simon Woodward Grazziela Figueredo Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis iScience Artificial intelligence Catalysis Chemistry |
| title | Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis |
| title_full | Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis |
| title_fullStr | Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis |
| title_full_unstemmed | Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis |
| title_short | Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis |
| title_sort | homogeneous catalyst graph neural network a human interpretable graph neural network tool for ligand optimization in asymmetric catalysis |
| topic | Artificial intelligence Catalysis Chemistry |
| url | http://www.sciencedirect.com/science/article/pii/S2589004225001415 |
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