{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T19:22:08Z","timestamp":1786044128670,"version":"3.56.0"},"reference-count":37,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2022,1,30]],"date-time":"2022-01-30T00:00:00Z","timestamp":1643500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000001","name":"US National Science Foundation","doi-asserted-by":"crossref","award":["IIS-1553687"],"award-info":[{"award-number":["IIS-1553687"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/100004917","name":"Cancer Prevention and Research Institute of Texas","doi-asserted-by":"publisher","award":["RP190107"],"award-info":[{"award-number":["RP190107"]}],"id":[{"id":"10.13039\/100004917","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,3,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>The crux of molecular property prediction is to generate meaningful representations of the molecules. One promising route is to exploit the molecular graph structure through graph neural networks (GNNs). Both atoms and bonds significantly affect the chemical properties of a molecule, so an expressive model ought to exploit both node (atom) and edge (bond) information simultaneously. Inspired by this observation, we explore the multi-view modeling with GNN (MVGNN) to form a novel paralleled framework, which considers both atoms and bonds equally important when learning molecular representations. In specific, one view is atom-central and the other view is bond-central, then the two views are circulated via specifically designed components to enable more accurate predictions. To further enhance the expressive power of MVGNN, we propose a cross-dependent message-passing scheme to enhance information communication of different views. The overall framework is termed as CD-MVGNN.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We theoretically justify the expressiveness of the proposed model in terms of distinguishing non-isomorphism graphs. Extensive experiments demonstrate that CD-MVGNN achieves remarkably superior performance over the state-of-the-art models on various challenging benchmarks. Meanwhile, visualization results of the node importance are consistent with prior knowledge, which confirms the interpretability power of CD-MVGNN.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The code and data underlying this work are available in GitHub at https:\/\/github.com\/uta-smile\/CD-MVGNN.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Supplementary information<\/jats:title>\n                  <jats:p>Supplementary data are available at Bioinformatics online.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac039","type":"journal-article","created":{"date-parts":[[2022,1,25]],"date-time":"2022-01-25T12:23:22Z","timestamp":1643113402000},"page":"2003-2009","source":"Crossref","is-referenced-by-count":74,"title":["Cross-dependent graph neural networks for molecular property prediction"],"prefix":"10.1093","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5971-0053","authenticated-orcid":false,"given":"Hehuan","family":"Ma","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Texas at Arlington , Arlington 76019, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yatao","family":"Bian","sequence":"additional","affiliation":[{"name":"AI Lab, Tencent , Shenzhen 518057, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu","family":"Rong","sequence":"additional","affiliation":[{"name":"AI Lab, Tencent , Shenzhen 518057, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenbing","family":"Huang","sequence":"additional","affiliation":[{"name":"Institute for AI Industry Research, Tsinghua University , Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingyang","family":"Xu","sequence":"additional","affiliation":[{"name":"AI Lab, Tencent , Shenzhen 518057, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weiyang","family":"Xie","sequence":"additional","affiliation":[{"name":"AI Lab, Tencent , Shenzhen 518057, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geyan","family":"Ye","sequence":"additional","affiliation":[{"name":"AI Lab, Tencent , Shenzhen 518057, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junzhou","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Texas at Arlington , Arlington 76019, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,1,30]]},"reference":[{"key":"2023020109010558600_btac039-B1","first-page":"1","author":"Bhal","year":"2007"},{"key":"2023020109010558600_btac039-B2","first-page":"549","article-title":"Rumor detection on social media with bi-directional graph convolutional networks","volume":"34","author":"Bian","year":"2020","journal-title":"Proc. AAAI Conf. Artif. Intell"},{"key":"2023020109010558600_btac039-B3","first-page":"2905","author":"Chang","year":"2021"},{"key":"2023020109010558600_btac039-B4","first-page":"2224","article-title":"Convolutional networks on graphs for learning molecular fingerprints","author":"Duvenaud","year":"2015","journal-title":"NeurIPS"},{"key":"2023020109010558600_btac039-B5","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1214\/aos\/1016218223","article-title":"Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)","volume":"28","author":"Friedman","year":"2000","journal-title":"Ann. Stat"},{"key":"2023020109010558600_btac039-B6","first-page":"1263","article-title":"Neural message passing for quantum chemistry","author":"Gilmer","year":"2017","journal-title":"ICML"},{"key":"2023020109010558600_btac039-B7","author":"Guo","year":"2020"},{"key":"2023020109010558600_btac039-B8","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/BF02854581","article-title":"Some properties of line digraphs","volume":"9","author":"Harary","year":"1960","journal-title":"Rend. Circ. Mat. Palermo"},{"key":"2023020109010558600_btac039-B9","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s10822-016-9938-8","article-title":"Molecular graph convolutions: moving beyond fingerprints","volume":"30","author":"Kearnes","year":"2016","journal-title":"J. Comput. Aided Mol. Des"},{"key":"2023020109010558600_btac039-B10","doi-asserted-by":"crossref","first-page":"D1202","DOI":"10.1093\/nar\/gkv951","article-title":"PubChem substance and compound databases","volume":"44","author":"Kim","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023020109010558600_btac039-B11","first-page":"972","volume-title":"TheWebConf","author":"Li","year":"2019"},{"key":"2023020109010558600_btac039-B12","first-page":"8464","article-title":"N-gram graph: simple unsupervised representation for graphs, with applications to molecules","author":"Liu","year":"2019","journal-title":"NeurIPS"},{"key":"2023020109010558600_btac039-B13","first-page":"1052","article-title":"Molecular property prediction: a multilevel quantum interactions modeling perspective","volume":"33","author":"Lu","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell"},{"key":"2023020109010558600_btac039-B14","doi-asserted-by":"crossref","first-page":"4066","DOI":"10.1021\/acs.jmedchem.5b00104","article-title":"pkCSM: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures","volume":"58","author":"Pires","year":"2015","journal-title":"J. Med. Chem"},{"key":"2023020109010558600_btac039-B15","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/978-3-030-28954-6_18","volume-title":"Explainable AI: Interpreting, Explaining and Visualizing Deep Learning","author":"Preuer","year":"2019"},{"key":"2023020109010558600_btac039-B16","first-page":"529","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"Raju","year":"2020"},{"key":"2023020109010558600_btac039-B17","doi-asserted-by":"crossref","first-page":"084111","DOI":"10.1063\/1.4928757","article-title":"Electronic spectra from TDDFT and machine learning in chemical space","volume":"143","author":"Ramakrishnan","year":"2015","journal-title":"J. Chem. Phys"},{"key":"2023020109010558600_btac039-B18","doi-asserted-by":"crossref","first-page":"1225","DOI":"10.1021\/acs.chemrestox.6b00135","article-title":"ToxCast chemical landscape: paving the road to 21st century toxicology","volume":"29","author":"Richard","year":"2016","journal-title":"Chem. Res. Toxicol"},{"key":"2023020109010558600_btac039-B19","article-title":"Self-supervised graph transformer on large-scale molecular data","author":"Rong","year":"2020","journal-title":"NeurIPS"},{"key":"2023020109010558600_btac039-B20","first-page":"991","article-title":"SchNet: a continuous-filter convolutional neural network for modeling quantum interactions","author":"Sch\u00fctt","year":"2017","journal-title":"NeurIPS"},{"key":"2023020109010558600_btac039-B21","first-page":"2831","author":"Song","year":"2020"},{"key":"2023020109010558600_btac039-B22","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1021\/ci8004379","article-title":"Influence relevance voting: an accurate and interpretable virtual high throughput screening method","volume":"49","author":"Swamidass","year":"2009","journal-title":"J. Chem. Inf. Model"},{"key":"2023020109010558600_btac039-B23","article-title":"Graph attention networks","author":"Veli\u010dkovi\u0107","year":"2018"},{"key":"2023020109010558600_btac039-B24","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1007\/978-3-030-20351-1_36","volume-title":"International Conference on Information Processing in Medical Imaging","author":"Wang","year":"2019"},{"key":"2023020109010558600_btac039-B25","first-page":"429","author":"Wang","year":"2019"},{"key":"2023020109010558600_btac039-B26","first-page":"12","article-title":"A reduction of a graph to a canonical form and an algebra arising during this reduction","volume":"2","author":"Weisfeiler","year":"1968","journal-title":"Nauchno Techn. Inform"},{"key":"2023020109010558600_btac039-B27","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1039\/C7SC02664A","article-title":"MoleculeNet: a benchmark for molecular machine learning","volume":"9","author":"Wu","year":"2018","journal-title":"Chem. Sci"},{"key":"2023020109010558600_btac039-B28","doi-asserted-by":"crossref","first-page":"8749","DOI":"10.1021\/acs.jmedchem.9b00959","article-title":"Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism","volume":"63","author":"Xiong","year":"2020","journal-title":"J. Med. Chem"},{"key":"2023020109010558600_btac039-B29","article-title":"How powerful are graph neural networks?","author":"Xu","year":"2018"},{"key":"2023020109010558600_btac039-B30","author":"Xu","year":"2017"},{"key":"2023020109010558600_btac039-B31","article-title":"RetroXpert: decompose retrosynthesis prediction like a chemist","author":"Yan","year":"2020"},{"key":"2023020109010558600_btac039-B32","first-page":"10603","author":"Yang","year":"2021"},{"key":"2023020109010558600_btac039-B33","doi-asserted-by":"crossref","first-page":"3370","DOI":"10.1021\/acs.jcim.9b00237","article-title":"Analyzing learned molecular representations for property prediction","volume":"59","author":"Yang","year":"2019","journal-title":"J. Chem. Inf. Model"},{"key":"2023020109010558600_btac039-B34","author":"Yu","year":"2020"},{"key":"2023020109010558600_btac039-B35","first-page":"7094","author":"Zeng","year":"2019"},{"key":"2023020109010558600_btac039-B36","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.inffus.2017.02.007","article-title":"Multi-view learning overview: recent progress and new challenges","volume":"38","author":"Zhao","year":"2017","journal-title":"Inf. Fusion"},{"key":"2023020109010558600_btac039-B37","first-page":"1","article-title":"Finding critical users in social communities via graph convolutions","author":"Zhao","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btac039\/42377820\/btac039.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/7\/2003\/49009479\/btac039.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/38\/7\/2003\/49009479\/btac039.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,1]],"date-time":"2023-02-01T20:44:54Z","timestamp":1675284294000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/38\/7\/2003\/6517516"}},"subtitle":[],"editor":[{"given":"Zhiyong","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2022,1,30]]},"references-count":37,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2022,3,28]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btac039","relation":{},"ISSN":["1367-4803","1367-4811"],"issn-type":[{"value":"1367-4803","type":"print"},{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,4,1]]},"published":{"date-parts":[[2022,1,30]]}}}