  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. We also experimentally
showed that semi-supervised tasks enhanced predictive performance compared with
supervised ones with labeled molecules only.
