MetalHawk: Enhanced Classification of Metal Coordination Geometries by Artificial Neural Networks

The chemical properties of metal complexes are strongly dependent on the number and geometrical arrangement of ligands coordinated to the metal center. Existing methods for determining either coordination number or geometry rely on a trade-off between accuracy and computational costs, which hinders...

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Bibliographic Details
Published in:Journal of chemical information and modeling 2024-04, Vol.64 (7), p.2356-2367
Main Authors: Sgueglia, Gianmattia, Vrettas, Michail D., Chino, Marco, De Simone, Alfonso, Lombardi, Angela
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Chemical properties
Classification
Coordination Complexes
Coordination compounds
Coordination numbers
Datasets
Geometric accuracy
Geometry
Machine learning
Machine Learning and Deep Learning
Metals - chemistry
Neural networks
Neural Networks, Computer
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Summary:The chemical properties of metal complexes are strongly dependent on the number and geometrical arrangement of ligands coordinated to the metal center. Existing methods for determining either coordination number or geometry rely on a trade-off between accuracy and computational costs, which hinders their application to the study of large structure data sets. Here, we propose MetalHawk (https://github.com/vrettasm/MetalHawk), a machine learning-based approach to perform simultaneous classification of metal site coordination number and geometry through artificial neural networks (ANNs), which were trained using the Cambridge Structural Database (CSD) and Metal Protein Data Bank (MetalPDB). We demonstrate that the CSD-trained model can be used to classify sites belonging to the most common coordination numbers and geometry classes with balanced accuracy equal to 96.51% for CSD-deposited metal sites. The CSD-trained model was also found to be capable of classifying bioinorganic metal sites from the MetalPDB database, with balanced accuracy equal to 84.29% on the whole PDB data set and to 91.66% on manually reviewed sites in the PDB validation set. Moreover, we report evidence that the output vectors of the CSD-trained model can be considered as a proxy indicator of metal-site distortions, showing that these can be interpreted as a low-dimensional representation of subtle geometrical features present in metal site structures.
ISSN:1549-9596
1549-960X
1549-960X
DOI:10.1021/acs.jcim.3c00873