Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel
arxiv(2023)
摘要
Graph Neural Networks (GNNs) have been widely adopted for drug discovery with
molecular graphs. Nevertheless, current GNNs mainly excel in 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 to abstract the collective information
of atomic groups into a few Neural Atoms by implicitly projecting
the atoms of a molecular. 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 to the traditional LRI
calculation method, Ewald Summation. The Neural Atom can enhance GNNs to
capture LRI by approximating the potential LRI of the molecular. We conduct
extensive experiments on four long-range graph benchmarks, covering graph-level
and link-level tasks on molecular graphs. We achieve up to a 27.32
improvement in the 2D and 3D scenarios, respectively. Empirically, our method
can be equipped with an arbitrary GNN to help capture LRI. Code and datasets
are publicly available in https://github.com/tmlr-group/NeuralAtom.
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