@inproceedings{
li2023longrange,
title={Long-Range Neural Atom Learning for Molecular Graphs},
author={Xuan Li and Zhanke Zhou and Jiangchao Yao and Yu Rong and Lu Zhang and Bo Han},
booktitle={NeurIPS 2023 Workshop: New Frontiers in Graph Learning},
year={2023},
url={https://openreview.net/forum?id=hySoEBuTuM}
}
Graph Neural Networks (GNNs) have been widely adopted for drug discovery with molecular graphs. Nevertheless, current GNNs are mainly good at leveraging short-range interactions (SRI) but struggle to capture long-range interactions (LRI), both of which are crucial for determining molecular properties. To tackle this issue, we propose a method that implicitly projects all original atoms into a few \textit{Neural Atoms}, which abstracts the collective information of atomic groups within a molecule. Specifically, we explicitly exchange the information among neural atoms and project them back to the atoms’ representations as an enhancement. With this mechanism, neural atoms establish the communication channels among distant nodes, effectively reducing the interaction scope of arbitrary node pairs into a single hop. To provide an inspection of our method from a physical perspective, we reveal its connection with the traditional LRI calculation method, Ewald Summation. We conduct extensive experiments on three long-range graph benchmarks, covering both graph-level and link-level tasks on molecular graphs. We empirically justify that our method can be equipped with an arbitrary GNN and help to capture LRI.
Long-Range Neural Atom Learning for Molecular Graphs
Xuan Li Zhanke Zhou Jiangchao Yao Yu Rong Lu Zhang Bo Han