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Outputs (3)

Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis (2025)
Journal Article
Aguilar-Bejarano, E., Özcan, E., Rit, R. K., Li, H., Lam, H. W., Moore, J. C., Woodward, S., & Figueredo, G. (2025). Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis. iScience, 28(3), Article 111881. https://doi.org/10.1016/j.isci.2025.111881

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 de... Read More about Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis.

ML meets MLn: machine learning in ligand promoted homogeneous catalysis (2023)
Journal Article
Hirst, J. D., Boobier, S., Coughlan, J., Streets, J., Jacob, P. L., Pugh, O., Özcan, E., & Woodward, S. (2023). ML meets MLn: machine learning in ligand promoted homogeneous catalysis. Artificial Intelligence Chemistry, 1(2), Article 100006. https://doi.org/10.1016/j.aichem.2023.100006

The benefits of using machine learning approaches in the design, optimisation and understanding of homogeneous catalytic processes are being increasingly realised. We focus on the understanding and implementation of key concepts, which serve as condu... Read More about ML meets MLn: machine learning in ligand promoted homogeneous catalysis.

Identifying Chirality in Line Drawings of Molecules Using Imbalanced Dataset Sampler for a Multilabel Classification Task (2022)
Journal Article
Kok, Y. E., Woodward, S., Özcan, E., & Torres Torres, M. (2022). Identifying Chirality in Line Drawings of Molecules Using Imbalanced Dataset Sampler for a Multilabel Classification Task. Molecular Informatics, 41(12), Article 2200068. https://doi.org/10.1002/minf.202200068

Chirality, the ability of some molecules to exist as two non-superimposable mirror images, profoundly influences both chemistry and biology. Advances in deep learning enable the automatic recognition of chemical structure diagrams, however, studies o... Read More about Identifying Chirality in Line Drawings of Molecules Using Imbalanced Dataset Sampler for a Multilabel Classification Task.