{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/semi-supervised-learning-of-hierarchical","title":"Semi-supervised learning of hierarchical representations of molecules using neural message passing","arxiv_id":"1711.10168","date":"2017-11-28","proceeding":null,"authors":["Hai Nguyen","Shin-ichi Maeda","Kenta Oono"],"abstract":"With the rapid increase of compound databases available in medicinal and\nmaterial science, there is a growing need for learning representations of\nmolecules in a semi-supervised manner. In this paper, we propose an\nunsupervised hierarchical feature extraction algorithm for molecules (or more\ngenerally, graph-structured objects with fixed number of types of nodes and\nedges), which is applicable to both unsupervised and semi-supervised tasks. Our\nmethod extends recently proposed Paragraph Vector algorithm and incorporates\nneural message passing to obtain hierarchical representations of subgraphs. We\napplied our method to an unsupervised task and demonstrated that it outperforms\nexisting proposed methods in several benchmark datasets. We also experimentally\nshowed that semi-supervised tasks enhanced predictive performance compared with\nsupervised ones with labeled molecules only.","url_abs":"http://arxiv.org/abs/1711.10168v2","url_pdf":"http://arxiv.org/pdf/1711.10168v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"semi-supervised-learning-of-hierarchical","repo_url":"https://github.com/pfnet-research/hierarchical-molecular-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1711.10168","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}