3 papers
cs.LG2025
Beyond Atoms: Evaluating Electron Density Representation for 3D Molecular Learning
Patricia Suriana, Joshua A. Rackers, Ewa M. Nowara +3
Machine learning models for 3D molecular property prediction typically rely on atom-based representations, which may overlook subtle physical information. Electron density maps --…
physics.bio-ph2025
JAMUN: Bridging Smoothed Molecular Dynamics and Score-Based Learning for Conformational Ensembles
Ameya Daigavane, Bodhi P. Vani, Darcy Davidson +3
Conformational ensembles of protein structures are immensely important both for understanding protein function and drug discovery in novel modalities such as cryptic pockets. Curre…
cs.LG2025
Do we need equivariant models for molecule generation?
Ewa M. Nowara, Joshua Rackers, Patricia Suriana +4
Deep generative models are increasingly used for molecular discovery, with most recent approaches relying on equivariant graph neural networks (GNNs) under the assumption that expl…