{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T17:58:48Z","timestamp":1772906328572,"version":"3.50.1"},"reference-count":27,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,6]]},"DOI":"10.1109\/bibm55620.2022.9995324","type":"proceedings-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T19:09:24Z","timestamp":1672686564000},"page":"374-379","source":"Crossref","is-referenced-by-count":11,"title":["CoAtGIN: Marrying Convolution and Attention for Graph-based Molecule Property Prediction"],"prefix":"10.1109","author":[{"given":"Xuan","family":"Zhang","sequence":"first","affiliation":[{"name":"Shandong University,School of Computer Science and Technology,Qingdao,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Shandong University,School of Computer Science and Technology,Qingdao,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhaoxu","family":"Meng","sequence":"additional","affiliation":[{"name":"Shandong University,School of Computer Science,Qingdao,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenghe","family":"Yang","sequence":"additional","affiliation":[{"name":"LTHPC (Beijing) Technology Company Limited,Beijing,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haitao","family":"Jiang","sequence":"additional","affiliation":[{"name":"Shandong University,School of Computer Science and Technology,Qingdao,China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuefeng","family":"Cui","sequence":"additional","affiliation":[{"name":"Shandong University,School of Computer Science and Technology,Qingdao,China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2022.02.015"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM52615.2021.9669276"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM52615.2021.9669781"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM52615.2021.9669846"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.3390\/proteomes4030028"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1039\/C2CS35348B"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/S0166-1280(99)00033-0"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1063\/1.4704546"},{"key":"ref9","article-title":"Ogb-lsc: A large-scale challenge for machine learning on graphs","author":"Hu","year":"2021","journal-title":"arXiv preprint arXiv:2103.09430"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00438-4"},{"key":"ref11","article-title":"Bert: Pre-training of deep bidirectional transformers for language understanding","author":"Devlin","year":"2018","journal-title":"arXiv preprint arXiv:1810.04805"},{"key":"ref12","article-title":"Improving language understanding with unsupervised learning","author":"Radford","year":"2018"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/3307339.3342186"},{"key":"ref14","article-title":"Chemberta: large-scale self-supervised pretraining for molecular property prediction","author":"Chithrananda","year":"2020","journal-title":"arXiv preprint arXiv:2010.09885"},{"key":"ref15","article-title":"Pure transformers are powerful graph learners","author":"Kim","year":"2022","journal-title":"arXiv preprint arXiv:2207.02505"},{"key":"ref16","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Advances in neural information processing systems"},{"key":"ref17","article-title":"How powerful are graph neural networks?","author":"Xu","year":"2018","journal-title":"arXiv preprint arXiv:1810.00826"},{"key":"ref18","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2017","journal-title":"arXiv preprint arXiv:1710.10903"},{"key":"ref19","first-page":"1263","article-title":"Neural message passing for quantum chemistry","volume-title":"International conference on machine learning","author":"Gilmer"},{"key":"ref20","article-title":"Deep-ergcn: All you need to train deeper gcns","author":"Li","year":"2020","journal-title":"arXiv preprint arXiv:2006.07739"},{"key":"ref21","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume":"33","author":"Hu","year":"2020","journal-title":"Advances in neural information processing systems"},{"key":"ref22","article-title":"cosformer: Rethinking softmax in attention","author":"Qin","year":"2022","journal-title":"arXiv preprint arXiv:2202.08791"},{"key":"ref23","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf","year":"2016","journal-title":"arXiv preprint arXiv:1609.02907"},{"key":"ref24","article-title":"Pure transformers are powerful graph learners","author":"Kim","year":"2022","journal-title":"arXiv preprint arXiv:2207.02505"},{"key":"ref25","article-title":"Grpe: Relative positional encoding for graph transformer","author":"Park","year":"2022","journal-title":"ICLR2022 Machine Learning for Drug Discovery"},{"key":"ref26","article-title":"Edge-augmented graph transformers: Global self-attention is enough for graphs","author":"Hussain","year":"2021","journal-title":"arXiv preprint arXiv:2108.03348"},{"key":"ref27","first-page":"28877","article-title":"Do transformers really perform badly for graph representation?","volume":"34","author":"Ying","year":"2021","journal-title":"Advances in Neural Information Processing Systems"}],"event":{"name":"2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","location":"Las Vegas, NV, USA","start":{"date-parts":[[2022,12,6]]},"end":{"date-parts":[[2022,12,8]]}},"container-title":["2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9994793\/9994847\/09995324.pdf?arnumber=9995324","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,14]],"date-time":"2024-03-14T03:35:12Z","timestamp":1710387312000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9995324\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,6]]},"references-count":27,"URL":"https:\/\/doi.org\/10.1109\/bibm55620.2022.9995324","relation":{},"subject":[],"published":{"date-parts":[[2022,12,6]]}}}