Learning Harmonic Molecular Representations on Riemannian Manifold

Understand

Molecular representation learning plays a crucial role in AI-assisted drug discovery research.

• Encoding 3D molecular structures through Euclidean neural networks has become the prevailing method in the geometric deep learning community.
• However, the equivariance constraints and message passing in Euclidean space may limit the network expressive power.
• In this work, we propose a Harmonic Molecular Representation learning (HMR) framework, which represents a molecule using the Laplace-Beltrami eigenfunctions of its molecular surface.