dc.contributor.author | Song, Y | |
dc.contributor.author | Zheng, S | |
dc.contributor.author | Niu, Z | |
dc.contributor.author | Fu, ZH | |
dc.contributor.author | Lu, Y | |
dc.contributor.author | Yang, Y | |
dc.date.accessioned | 2021-01-29T03:25:20Z | |
dc.date.available | 2021-01-29T03:25:20Z | |
dc.date.issued | 2020 | |
dc.identifier.isbn | 9780999241165 | |
dc.identifier.issn | 1045-0823 | |
dc.identifier.doi | 10.24963/ijcai.2020/392 | |
dc.identifier.uri | http://hdl.handle.net/10072/401561 | |
dc.description.abstract | Constructing proper representations of molecules lies at the core of numerous tasks such as molecular property prediction and drug design. Graph neural networks, especially message passing neural network (MPNN) and its variants, have recently made remarkable achievements in molecular graph modeling. Albeit powerful, the one-sided focuses on atom (node) or bond (edge) information of existing MPNN methods lead to the insufficient representations of the attributed molecular graphs. Herein, we propose a Communicative Message Passing Neural Network (CMPNN) to improve the molecular embedding by strengthening the message interactions between nodes and edges through a communicative kernel. In addition, the message generation process is enriched by introducing a new message booster module. Extensive experiments demonstrated that the proposed model obtained superior performances against state-of-the-art baselines on six chemical property datasets. Further visualization also showed better representation capacity of our model. | |
dc.description.peerreviewed | Yes | |
dc.publisher | International Joint Conferences on Artificial Intelligence Organization | |
dc.relation.ispartofconferencename | 29th International Joint Conference on Artificial Intelligence and the 17th Pacific Rim International Conference on Artificial Intelligence (IJCAI-PRICAI2020) | |
dc.relation.ispartofconferencetitle | Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence | |
dc.relation.ispartofdatefrom | 2021-01-07 | |
dc.relation.ispartofdateto | 2021-01-15 | |
dc.relation.ispartoflocation | Yokohama, Japan | |
dc.relation.ispartofpagefrom | 2831 | |
dc.relation.ispartofpageto | 2838 | |
dc.subject.fieldofresearch | Artificial intelligence | |
dc.subject.fieldofresearchcode | 4602 | |
dc.title | Communicative representation learning on attributed molecular graphs | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dcterms.bibliographicCitation | Song, Y; Zheng, S; Niu, Z; Fu, ZH; Lu, Y; Yang, Y, Communicative representation learning on attributed molecular graphs, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020, 2021-January, pp. 2831-2838 | |
dc.date.updated | 2021-01-29T03:22:46Z | |
dc.description.version | Version of Record (VoR) | |
gro.rights.copyright | © 2021 International Joint Conference on Artificial Intelligence. The attached file is reproduced here in accordance with the copyright policy of the publisher. Please refer to the Conference's website for access to the definitive, published version. | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Yang, Yuedong | |