A Comprehensive Machine Learning Model for Metal–Ligand Binding Prediction: Applications in Chemistry and Biology

A machine-learning (ML) model that predicts metal–ligand
binding constants was developed using the open-source Chemprop software.
The model was trained on over 30,000 experimental log K1 values, which include both protonation and metal–ligand
stability constants, comprising over 3500 ligands and 102 metal ions from 73 total elements, thus generalizing beyond existing
limited approaches, which focus only on specific metals or ligand
families. The best-performing model included a combination of SMILES-based
molecular representations along with descriptors for the metal ion
and experimental conditions. It had an external test R2 value of 0.942, and MAE value of 0.834. A “SMILES-only”
simpler version also produced accurate predictions and preserved the
binding trends, serving as a quick and easily accessible alternative
for users without computational expertise. The SMILES-only model performed
comparably to density functional theory (DFT) calculations but utilized
a fraction of the computational resources. The model was successfully
applied across diverse domains, including bioinorganic chemistry,
heavy metal remediation, and sensor development and demonstrated its
effectiveness as a rapid and reliable screening tool for both academic
and industrial uses.