论文标题
分子力学驱动的图形神经网络，具有分子结构的多重图
Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
论文作者
论文摘要
从分子结构中对物理化学特性的预测是人工智能辅助分子设计的至关重要任务。已经提出了越来越多的图形神经网络（GNN）来应对这一挑战。这些模型通过将辅助信息纳入分子中，同时不可避免地提高其计算复杂性，从而提高其表达能力。在这项工作中，我们旨在设计一种既有强大又高效的分子结构的GNN。为了实现此类目标，我们通过首先将每个分子作为两层多路复用图提出了一种分子力学驱动的方法，其中一个层仅包含局部连接，这些局部连接主要捕获共价相互作用，而另一层包含可以模拟非共价相互作用的全局连接。然后，对于每一层，提出了一个相应的消息传递模块，以平衡表达能力和计算复杂性的权衡。基于这两个模块，我们构建了多重分子图神经网络（MXMNET）。当通过QM9数据集用于小分子和PDBBIND数据集的大型蛋白质配体配合物时，MXMNET在受限资源下的现有最新模型取得了优于现有的最新模型的结果。
The prediction of physicochemical properties from molecular structures is a crucial task for artificial intelligence aided molecular design. A growing number of Graph Neural Networks (GNNs) have been proposed to address this challenge. These models improve their expressive power by incorporating auxiliary information in molecules while inevitably increase their computational complexity. In this work, we aim to design a GNN which is both powerful and efficient for molecule structures. To achieve such goal, we propose a molecular mechanics-driven approach by first representing each molecule as a two-layer multiplex graph, where one layer contains only local connections that mainly capture the covalent interactions and another layer contains global connections that can simulate non-covalent interactions. Then for each layer, a corresponding message passing module is proposed to balance the trade-off of expression power and computational complexity. Based on these two modules, we build Multiplex Molecular Graph Neural Network (MXMNet). When validated by the QM9 dataset for small molecules and PDBBind dataset for large protein-ligand complexes, MXMNet achieves superior results to the existing state-of-the-art models under restricted resources.
