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

ABSTRACT: 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 preserve

SUBMITTER: Karunaratne E 

PROVIDER: S-EPMC12606636 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature