Procuring expressive molecular representations underpins AI-driven molecule design and scientific discovery. The research to date mainly focuses on atom-level homogeneous molecular graphs, ignoring the rich information in subgraphs or motifs. However, it has been widely accepted that substructures play a dominant role in the identification and determination of molecular properties. To address such issues, we formulate heterogeneous molecular graphs (HMGs), and introduce Molformer to exploit both molecular motifs and 3D geometry. Specifically, we extract functional groups as motifs for small molecules and resort to the reinforcement learning to adaptively select quaternary amino acids as motifs for proteins. Then HMGs are constructed with both atom-level and motif-level nodes. To better accommodate those HMGs, we introduce a variant of Transformer named Molformer, which adopts a heterogeneous self-attention layer to distinguish the interactions between multi-level nodes. Besides, it is also coupled with a multi-scale mechanism to capture local fine-grained patterns with increasing contextual scales. An attentive farthest point sampling algorithm is also proposed to obtain the molecular representations. We validate Molformer across a few domains including quantum chemistry, physiology, and biophysics. Experiments show that Molformer outperforms state-of-the-art baselines. Our work provides a promising way to utilize informative motifs from the perspective of multi-level graph construction.

翻译：为了解决这些问题,我们制作了异质分子图(HMGs),并引入了Molfer, 以利用分子模块和3D几何方法的相互作用。具体地说,我们从功能组中提取小分子的模型,并采用强化学习适应性选择的四硝基氨酸作为蛋白质的模型。然后,HMGs用原子层面和motif级节点来构建。为了更好地容纳这些分子特性,我们引入了名为Molder的变异器变异式分子图(HMGs),该变异式自我保存层用来区分多层次节点之间的相互作用。此外,我们还利用一个多尺度机制,用适应性选择的四硝基氨酸作为蛋白质的模型学习。然后,HMGs用原子层面和motif级节点节点节点节点节点的节点来构建。为了更好地容纳这些分子特性,我们提出了一个有希望的模型的模型模型模型模型,我们从数级级的模型到数级级的模型。