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. HMR offers a multi-resolution representation of molecular
geometric and chemical features on 2D Riemannian manifold. We also introduce a
harmonic message passing method to realize efficient spectral message passing
over the surface manifold for better molecular encoding. Our proposed method
shows comparable predictive power to current models in small molecule property
prediction, and outperforms the state-of-the-art deep learning models for
ligand-binding protein pocket classification and the rigid protein docking
challenge, demonstrating its versatility in molecular representation learning.

本研究提出了一种谐波分子表示学习方法，使用分子表面的 Laplace-Beltrami 特征将分子表示为二维黎曼曼 ifold 上的多分辨率表示，同时采用谐波消息传递方法实现更好的分子编码，达到了与当前模型相当的预测能力，优于最先进的深度学习模型的生物蛋白质口袋分类和刚性蛋白质对接挑战，展示了其在分子表示学习中的多功能性。