As they carry great potential for modeling complex interactions, graph neural
network (GNN)-based methods have been widely used to predict quantum mechanical
properties of molecules. Most of the existing methods treat molecules as
molecular graphs in which atoms are modeled as nodes. They characterize each
atom's chemical environment by modeling its pairwise interactions with other
atoms in the molecule. Although these methods achieve a great success, limited
amount of works explicitly take many-body interactions, i.e., interactions
between three and more atoms, into consideration. In this paper, we introduce a
novel graph representation of molecules, heterogeneous molecular graph (HMG) in
which nodes and edges are of various types, to model many-body interactions.
HMGs have the potential to carry complex geometric information. To leverage the
rich information stored in HMGs for chemical prediction problems, we build
heterogeneous molecular graph neural networks (HMGNN) on the basis of a neural
message passing scheme. HMGNN incorporates global molecule representations and
an attention mechanism into the prediction process. The predictions of HMGNN
are invariant to translation and rotation of atom coordinates, and permutation
of atom indices. Our model achieves state-of-the-art performance in 9 out of 12
tasks on the QM9 dataset.

本文提出了一种名为 “异构分子图神经网络” 的方法，通过建立多种类型的节点和边来构建分子的异构分子图，并考虑到三个或更多原子之间的相互作用，结合神经信息传递机制进行对比化学性质预测，该方法在 QM9 数据集中的 12 个任务中有 9 个达到最优表现。