Hybrid Computational Strategy for Predicting Complex Ligand–Metal Architectures

Abstract

ABSTRACT 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.