ConfDENSE: A conformer aware electron density based machine learning paradigm for navigating the odorant landscape

Olfaction arises from the interaction of odorants with olfactory receptors, a process shaped by molecular geometry, electron distribution, and conformational preference. We present ConfDENSE, a Set2Set enhanced PointNet model that learns directly from Hirshfeld promolecule electron-density point clouds, preserving full 3D electronic in- formation without downsampling.Despite using no receptor structural data, ConfDENSE accurately identifies bioac- tive conformers from ensemble inputs. For the only available human odorant receptor structures, the model’s selected conformers achieve sub-angstrom RMSDs to crystallo- graphic ligand poses and frequently outperform conventional docking. Combining ConfDENSE with explainability analysis further reveals the substruc- tures most responsible for receptor engagement, aligning with experimental interaction patterns. This ligand-centric and interpretable framework naturally supports phar- macophore extraction and scaffold-based design, enabling identification of conserved binding motifs even when receptor structures are missing. ConfDENSE thus provides a compact, physics-aware approach to computational olfaction, linking electron density, conformational preference, and odorant recognition in a structurally agnostic manner.