Understanding how metals coordinate to organic ligands is a precondition for the rational design of metal complexes and catalysts. Whereas certain types of ligands are capable of just one easy-to-predict coordination modality, others may present tens and sometimes even hundreds of coordination options (mono-, bi-, or polydentate), and predicting the correct one may be a challenge even to seasoned chemists. The current paper describes a "hybrid" computational approach in which a Machine Learning, ML, algorithm learns to predict complex coordination patterns using knowledge-based "rules" derived from the Cambridge Structural Database, CSD. This model is applicable to a broad scope of ligands (including hemilabile and haptic ones as well as those with denticity > 6) and different metals at different oxidation states. The algorithm's code is disclosed and can be readily deployed in RDKit via our RDMetallics python-wrapper. It is also deployed as a publicly accessible web portal for demonstration and use.
Moldagulov et al. (Sat,) studied this question.