Chemical and geometric representation fidelity tied to better drug-target affinity models

A new arXiv paper argues that drug-target binding affinity prediction improves when models preserve both chemical and geometric details of the interacting molecules. The authors focus on representation fidelity as a way to capture the subtle structural features that determine molecular recognition. The work falls under machine learning research rather than a released product or model.