Semi-supervised learning of hierarchical representations of molecules using neural message passing

Understand

With the rapid increase of compound databases available in medicinal and material science, there is a growing need for learning representations of molecules in a semi-supervised manner.

• In this paper, we propose an unsupervised hierarchical feature extraction algorithm for molecules (or more generally, graph-structured objects with fixed number of types of nodes and edges), which is applicable to both unsupervised and semi-supervised tasks.
• Our method extends recently proposed Paragraph Vector algorithm and incorporates neural message passing to obtain hierarchical representations of subgraphs.
• We applied our method to an unsupervised task and demonstrated that it outperforms existing proposed methods in several benchmark datasets.