Simplicial Message Passing for Chemical Property Prediction

TL;DR

This paper introduces a Simplicial Message Passing framework that enhances molecular graph neural networks by capturing complex topological information, leading to improved quantum-chemical property predictions.

Contribution

It proposes a novel SMP framework that incorporates higher-order simplices to better represent molecular structures, surpassing traditional MPNNs.

Findings

Achieved state-of-the-art results in quantum-chemical property prediction.

Higher-order simplices improve the capture of molecular topology.

Enhanced performance over traditional message-passing neural networks.

Abstract

Recently, message-passing Neural networks (MPNN) provide a promising tool for dealing with molecular graphs and have achieved remarkable success in facilitating the discovery and materials design with desired properties. However, the classical MPNN methods also suffer from a limitation in capturing the strong topological information hidden in molecular structures, such as nonisomorphic graphs. To address this problem, this work proposes a Simplicial Message Passing (SMP) framework to better capture the topological information from molecules, which can break through the limitation within the vanilla message-passing paradigm. In SMP, a generalized message-passing framework is established for aggregating the information from arbitrary-order simplicial complex, and a hierarchical structure is elaborated to allow information exchange between different order simplices. We apply the SMP framework within deep learning architectures for quantum-chemical properties prediction and achieve state-of-the-art results. The results show that compared to traditional MPNN, involving higher-order simplex can better capture the complex structure of molecules and substantially enhance the performance of tasks. The SMP-based model can provide a generalized framework for GNNs and aid in the discovery and design of materials with tailored properties for various applications.