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.

该论文提出了一种基于分子力学的方法，通过将每个分子表示为一个双层多重图，并为每层提出相应的消息传递模块，设计了一种高效且表达能力强的分子结构图神经网络 MXMNet，可用于从分子结构预测物理化学性质，获得了优越的结果。

基于分子力学驱动的多重图图神经网络用于分子结构

Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for  Molecular Structures

Many complex natural and cultural phenomena are well modelled by systems of
simple interactions between particles. A number of architectures have been
developed to articulate this kind of structure, both implicitly and explicitly.
We consider an unsupervised explicit model, the NRI model, and make a series of
representational adaptations and physically motivated changes. Most notably we
factorise the inferred latent interaction graph into a multiplex graph,
allowing each layer to encode for a different interaction-type. This fNRI model
is smaller in size and significantly outperforms the original in both edge and
trajectory prediction, establishing a new state-of-the-art. We also present a
simplified variant of our model, which demonstrates the NRI's formulation as a
variational auto-encoder is not necessary for good performance, and make an
adaptation to the NRI's training routine, significantly improving its ability
to model complex physical dynamical systems.

简述：通过在 NRI 模型中引入一种新的因子化方法，即将推断的潜在交互图分解为一种多层结构，使 fNRI 模型在边缘预测和轨迹预测方面表现明显优于原模型，同时也提出了模型的简化版，并对训练例程进行调整以大大改善其对复杂物理动态系统的建模能力。