Graph neural networks for predicting metal-ligand coordination of transition metal complexes.

ABSTRACT: High-throughput virtual screening campaigns are invaluable for surveying the combinatorial space of possible transition metal complexes (TMCs), but they rely on accurate metal-ligand connectivity for meaningful results. Here, we curate a dataset of 70,069 unique ligands of known coordination from experimental structures of TMCs deposited in the Cambridge Structural Database. Using this dataset, we train separate graph neural network models to predict the total number and individual identities of ligand coordinating atoms with high accuracy and precision. Interpreting each model in terms of the learned molecular representations uncovers trends aligned with our understanding of coordination chemistry as well as chemical insights. Next, we integrate the trained models with the high-throughput

SUBMITTER: Toney JW 

PROVIDER: S-EPMC12541316 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature