Recent advances in applying Graph Neural Networks (GNNs) to molecular science have showcased the power of learning three-dimensional (3D) structure representations with GNNs. However, most existing GNNs suffer from the limitations of insufficient modeling of diverse interactions, computational expensive operations, and ignorance of vectorial values. Here, we tackle these limitations by proposing a novel GNN model, Physics-aware Multiplex Graph Neural Network (PaxNet), to efficiently and accurately learn the representations of 3D molecules for both small organic compounds and macromolecule complexes. PaxNet separates the modeling of local and non-local interactions inspired by molecular mechanics, and reduces the expensive angle-related computations. Besides scalar properties, PaxNet can also predict vectorial properties by learning an associated vector for each atom. To evaluate the performance of PaxNet, we compare it with state-of-the-art baselines in two tasks. On small molecule dataset for predicting quantum chemical properties, PaxNet reduces the prediction error by 15% and uses 73% less memory than the best baseline. On macromolecule dataset for predicting protein-ligand binding affinities, PaxNet outperforms the best baseline while reducing the memory consumption by 33% and the inference time by 85%. Thus, PaxNet provides a universal, robust and accurate method for large-scale machine learning of molecules. Our code is available at https://github.com/zetayue/Physics-aware-Multiplex-GNN.

翻译：近期将图神经网络（GNN）应用于分子科学的研究进展，展示了利用GNN学习三维结构表征的强大能力。然而，现有大多数GNN存在对多样化相互作用建模不足、计算开销昂贵以及忽略矢量值等局限性。针对这些局限，本文提出了一种新型GNN模型——物理感知多路图神经网络（PaxNet），旨在高效且精确地学习三维分子表征，适用于小分子有机化合物与高分子复合物。受分子力学启发，PaxNet将局部与非局部相互作用分离建模，并减少了与角度相关的昂贵计算。除了标量属性，PaxNet还能通过学习每个原子的关联矢量来预测矢量属性。为评估PaxNet性能，我们在两项任务中与现有最优基线模型进行了比较。在预测量子化学性质的小分子数据集上，PaxNet将预测误差降低了15%，内存使用量较最佳基线减少73%。在预测蛋白质-配体结合亲和力的大分子数据集上，PaxNet在内存消耗降低33%、推理时间缩短85%的同时，性能优于最佳基线。因此，PaxNet为大规模分子机器学习提供了一种通用、稳健且精确的方法。我们的代码开源于 https://github.com/zetayue/Physics-aware-Multiplex-GNN。