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

ABSTRACT: 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 co

SUBMITTER: Sgueglia G 

PROVIDER: S-EPMC11005052 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature