Semi-supervised learning of hierarchical representations of molecules using neural message passing
This work addresses the need for efficient molecule representation learning in medicinal and material science, though it appears incremental as it builds on existing algorithms like Paragraph Vector.
The paper tackles the problem of learning hierarchical representations of molecules from large compound databases in a semi-supervised manner, and it demonstrates improved performance over existing methods on benchmark datasets and enhanced predictive performance in semi-supervised tasks compared to supervised-only approaches.
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.