Molecular representation learning is a crucial task in predicting molecular
properties. Molecules are often modeled as graphs where atoms and chemical
bonds are represented as nodes and edges, respectively, and Graph Neural
Networks (GNNs) have been commonly utilized to predict atom-related properties,
such as reactivity and solubility. However, functional groups (subgraphs) are
closely related to some chemical properties of molecules, such as efficacy, and
metabolic properties, which cannot be solely determined by individual atoms. In
this paper, we introduce a new model for molecular representation learning
called the Atomic and Subgraph-aware Bilateral Aggregation (ASBA), which
addresses the limitations of previous atom-wise and subgraph-wise models by
incorporating both types of information. ASBA consists of two branches, one for
atom-wise information and the other for subgraph-wise information. Considering
existing atom-wise GNNs cannot properly extract invariant subgraph features, we
propose a decomposition-polymerization GNN architecture for the subgraph-wise
branch. Furthermore, we propose cooperative node-level and graph-level
self-supervised learning strategies for ASBA to improve its generalization. Our
method offers a more comprehensive way to learn representations for molecular
property prediction and has broad potential in drug and material discovery
applications. Extensive experiments have demonstrated the effectiveness of our
method.

本文通过引入 Atomic and Subgraph-aware Bilateral Aggregation 模型，综合考虑原子和子图两种信息，提高分子性质预测的表示学习，该方法在药物和材料发现等领域具有广泛的应用。