Learning Harmonic Molecular Representations on Riemannian ManifoldDownload PDF

Published: 01 Feb 2023, Last Modified: 14 Jul 2024ICLR 2023 posterReaders: Everyone
Keywords: Riemannian manifold, molecular surface, harmonic analysis, functional map, binding site prediction, rigid protein docking
TL;DR: We propose a harmonic molecular representation learning framework to achieve multi-resolution molecular encoding on 2D Riemannian manifold.
Abstract: 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 the molecular surface. HMR offers a multi-resolution representation of molecular geometric and chemical properties 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 the rigid protein docking challenge, demonstrating its versatility in molecular representation learning.
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors’ identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics
Submission Guidelines: Yes
Please Choose The Closest Area That Your Submission Falls Into: Machine Learning for Sciences (eg biology, physics, health sciences, social sciences, climate/sustainability )
Supplementary Material: zip
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 6 code implementations](https://www.catalyzex.com/paper/learning-harmonic-molecular-representations/code)
19 Replies

Loading