Symphony: Symmetry-Equivariant Point-Centered Spherical Harmonics for 3D Molecule Generation
Authors: Ameya Daigavane,Song Eun Kim,Mario Geiger,Tess Smidt
Presentation: ICLR 2024,Poster

Abstract: We present Symphony, an $E(3)$ equivariant autoregressive generative model for 3D molecular geometries
that iteratively builds a molecule from molecular fragments.
Existing autoregressive models such as G-SchNet and G-SphereNet  for molecules utilize rotationally invariant features to respect the 3D symmetries of molecules.
In contrast, Symphony uses message-passing with higher-degree $E(3)$-equivariant features.
This allows a novel representation of probability distributions via spherical harmonic signals to efficiently model the 3D geometry of
molecules. We show that Symphony is able to accurately generate small molecules from the QM9 dataset, outperforming existing
autoregressive models and approaching the performance of diffusion models. Our code is available at https://github.com/atomicarchitects/symphony.