Title: ED-BFN: Electron Density Point Clouds Enable High-Fidelity 3D Molecular Generation via GeoBFN
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: 3D molecular generation, electron density, Bayesian Flow Networks, point cloud

Abstract: Designing molecules that complement the 3D shape and chemical environment of their biological target is a central challenge in drug design. While current generative models often rely on discrete atomic coordinates or abstract ligand features, they struggle to capture the continuous, interaction-driven nature of molecular recognition. To bridge this gap, we propose leveraging electron density (ED) — a continuous, physics-grounded representation encoding conformational ensembles and local chemical environments. We introduce \textbf{ED-BFN}, an SE(3)-equivariant generative model that generates 3D molecular structures conditioned on sparse, pharmacophore-annotated point clouds derived from ligand ED. This approach provides a structure-aware prior without precise atom coordinates of protein structures. Unlike existing ED-based models, ED-BFN maintains strict spatial fidelity by aligning generated atoms with underlying electronic features. Evaluated on the DUD-E benchmark, ED-BFN recovers 37/101 bioactive molecules under oracle setting (with ground-truth atom count provided) and 28/101 in fully end-to-end setting (atom count predicted from electron density integral). Furthermore,45.7% of generated poses achieve better pocket binding compared to redocking poses. To our knowledge, ED-BFN is the first ED-based generative model to represent electron density , and currently achieves state-of-the-art performance on bioactive molecule recovery and docking score improvement.

TL;DR: ED-BFN: First BFN-based model generating 3D molecules from electron density point clouds. On DUD-E: 37/101 bioactive recovery (oracle), 28/101 end-to-end, 45.7% poses > redock — beats all ED-driven & pocket-aware SBDD models.