Enriching molecular graph representation via substructure learning

Continuous molecular graph representations are highly useful for effective molecule property predictions. However, learning graph-specific structure information remains challenging. Current graph neural network models typically aggregate all node embeddings of a graph into a single vector, which, while being an efficient compressor, may not capture the high-level function-related substructure information, e.g., motifs. Moreover, the nodes that are loosely relevant to the graph representation may lead to the sub-optimal representation learning. Towards these limitations, in this paper we propose a new approach to enrich molecular graph representation, where the most relevant motif are selected to represent the graph at a high level and the key-node spanned subgraph is constructed to filter out irrelevant nodes. By comparing to recently proposed unimodal graph neural network models and multi-modal methods, we show that our method achieves state-of-the-art performance on molecular property prediction task.