Collision-free morgan fingerprints: a principled approach to enhance machine learning performance and interpretability in chemistry

The success of machine learning in chemistry is fundamentally underpinned by the information fidelity of molecular representations. Despite their widespread adoption for efficiency and interpretability, Morgan fingerprints harbor a long-overlooked and fundamental flaw: bit collisions. This phenomenon erroneously maps distinct chemical substructures to identical positions, systematically corrupting structure-property relationships and severely compromising model interpretability. To address this