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 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.

翻译：图神经网络（GNNs）已被广泛应用于基于分子图的药物发现领域。然而，当前GNNs主要擅长利用短程相互作用（Short-Range Interactions, SRI），但在捕捉长程相互作用（Long-Range Interactions, LRI）方面存在困难，而这两类相互作用对于确定分子性质都至关重要。为解决这一问题，我们提出了一种方法，该方法隐式地将所有原始原子投影到少量神经原子（Neural Atoms）中，这些神经原子抽象了分子内原子基团的集体信息。具体而言，我们显式地在神经原子之间交换信息，并将它们投影回原子的表示中以实现增强。通过这种机制，神经原子在远距离节点之间建立了通信通道，有效地将任意节点对的交互范围缩减为单跳。为了从物理角度提供对方法的审视，我们揭示了其与传统长程相互作用计算方法——埃瓦尔德求和（Ewald Summation）的联系。我们在三个长程图基准数据集上进行了广泛实验，涵盖了分子图上的图级和链接级任务。实验证明，我们的方法可以集成到任意GNN中，并有助于捕捉长程相互作用。