A comprehensive model to predict metal ion binding constants

Ligand – metal binding scenarios in which the expanded model can be used.
Ligand – metal binding scenarios in which the expanded model can be used.

CMI researchers from Ames National Laboratory conducted the activity for this highlight

Innovation 
Developed a cutting-edge machine learning (ML) model to accurately predict proton and metal-ligand binding constants for many metals and ligands.

Achievement
Trained on a diverse metal ions and ligands dataset, this model demonstrates greater generality, surpassing the performance of existing models limited to specific ligand and metal classes. 

Significance and Impact
The remarkable performance using only Simplified Molecular-Input Line-Entry System (SMILES) strings as input allows accessibility to non-computational experts.
The next step will be to demonstrate the model's power by validating with new molecule design.

Hub Target Addressed 
Highly selective extraction and separations. Increasing the speed of discovery and integration. Developing and applying scientific tools to accelerate technology maturation.