Coordination-informed machine learning enables virtual screening of phenanthroline ligands by predicting Am/Eu binding preferences

A coordination-informed machine-learning workflow transfers 3D/quantum-chemical knowledge into design-stage ligand screening, enabling rapid Am/Eu binding-preference prediction and prioritization in a 300 000-member phenanthroline-derived library.