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（GNN）已广泛应用于基于分子图的药物发现。然而，当前GNN主要擅长利用短程相互作用（SRI），但在捕捉长程相互作用（LRI）方面存在困难，而这两者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，该方法将所有原始原子隐式投影到少量神经原子（Neural Atoms）上，从而抽象分子内原子群的集体信息。具体来说，我们在神经原子之间显式交换信息，并将其投影回原子表示以作为增强。通过这一机制，神经原子在远距离节点间建立通信通道，有效将任意节点对的交互范围缩减为单跳。为从物理角度审视我们的方法，我们揭示了其与传统LRI计算方法——埃瓦尔德求和（Ewald Summation）之间的联系。我们在三个长程图基准上进行了广泛实验，涵盖分子图的图级和链接级任务。实验证明，我们的方法可适配任意GNN，并有效帮助捕捉LRI。