{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T03:56:26Z","timestamp":1784174186252,"version":"3.55.0"},"reference-count":32,"publisher":"Oxford University Press (OUP)","issue":"10","license":[{"start":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T00:00:00Z","timestamp":1728604800000},"content-version":"vor","delay-in-days":10,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["NSFC 62276099"],"award-info":[{"award-number":["NSFC 62276099"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018542","name":"Natural Science Foundation of Sichuan Province","doi-asserted-by":"publisher","award":["2023NSFSC0501"],"award-info":[{"award-number":["2023NSFSC0501"]}],"id":[{"id":"10.13039\/501100018542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Predicting the properties of molecules is a fundamental problem in drug design and discovery, while how to learn effective feature representations lies at the core of modern deep-learning-based prediction methods. Recent progress shows expressive power of graph neural networks (GNNs) in capturing structural information for molecular graphs. However, we find that most molecular graphs exhibit low clustering along with dominating chains. Such topological characteristics can induce feature squashing during message passing and thus impair the expressivity of conventional GNNs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>Aiming at improving node features\u2019 expressiveness, we develop a novel chain-aware graph neural network model, wherein the chain structures are captured by learning the representation of the center node along the shortest paths starting from it, and the redundancy between layers are mitigated via initial residual difference connection (IRDC). Then the molecular graph is represented by attentive pooling of all node representations. Compared to standard graph convolution, our chain-aware learning scheme offers a more straightforward feature interaction between distant nodes, thus it is able to capture the information about long-range dependency. We provide extensive empirical analysis on real-world datasets to show the outperformance of the proposed method.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>The MolPath code is publicly available at https:\/\/github.com\/Assassinswhh\/Molpath.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btae574","type":"journal-article","created":{"date-parts":[[2024,10,11]],"date-time":"2024-10-11T17:15:22Z","timestamp":1728666922000},"source":"Crossref","is-referenced-by-count":13,"title":["Chain-aware graph neural networks for molecular property prediction"],"prefix":"10.1093","volume":"40","author":[{"given":"Honghao","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Software Engineering, Southwest Petroleum University , Chengdu 610500,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Acong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Software Engineering, Southwest Petroleum University , Chengdu 610500,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Computer Science and Software Engineering, Southwest Petroleum University , Chengdu 610500,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junlei","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Chemistry and Chemical Engineering, Southwest Petroleum University , Chengdu 610500,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, East China Normal University , Shanghai 200062,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8391-6510","authenticated-orcid":false,"given":"Ping","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Software Engineering, Southwest Petroleum University , Chengdu 610500,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,10,11]]},"reference":[{"key":"2024102209543214300_btae574-B1","doi-asserted-by":"publisher","first-page":"525","volume-title":"Nat Mach Intell","DOI":"10.1038\/s42256-024-00832-8"},{"key":"2024102209543214300_btae574-B3","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1186\/s13321-020-00460-5","article-title":"Molecular representations in ai-driven drug discovery: a review and practical guide","volume":"12","author":"David","year":"2020","journal-title":"J Cheminform"},{"key":"2024102209543214300_btae574-B4","doi-asserted-by":"publisher","first-page":"1503","DOI":"10.1002\/cmdc.200800178","article-title":"On the art of compiling and using \u2018drug-like\u2019chemical fragment spaces","volume":"3","author":"Degen","year":"2008","journal-title":"ChemMedChem"},{"key":"2024102209543214300_btae574-B5","author":"Devlin"},{"key":"2024102209543214300_btae574-B6","author":"Edwards"},{"key":"2024102209543214300_btae574-B7","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1038\/s42256-021-00438-4","article-title":"Geometry-enhanced molecular representation learning for property prediction","volume":"4","author":"Fang","year":"2022","journal-title":"Nat Mach Intell"},{"key":"2024102209543214300_btae574-B8","first-page":"566","author":"Giraldo","year":"2023"},{"key":"2024102209543214300_btae574-B9","article-title":"Inductive representation learning on large graphs","author":"Hamilton"},{"key":"2024102209543214300_btae574-B10","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"2024102209543214300_btae574-B11","author":"Huang","year":"2023"},{"key":"2024102209543214300_btae574-B12","first-page":"1","article-title":"Revisiting the role of heterophily in graph representation learning: An edge classification perspective","volume":"18","author":"Huang","year":"2023","journal-title":"ACM Trans Knowl Discov Data"},{"key":"2024102209543214300_btae574-B13","author":"Huang","year":"2024"},{"key":"2024102209543214300_btae574-B14","first-page":"015022","article-title":"Chemformer: a pre-trained transformer for computational chemistry","volume":"3","author":"Irwin","year":"2022","journal-title":"Mach Learn: Sci Technol"},{"key":"2024102209543214300_btae574-B15","author":"Kipf"},{"key":"2024102209543214300_btae574-B16","doi-asserted-by":"publisher","first-page":"100588","DOI":"10.1016\/j.patter.2022.100588","article-title":"Selfies and the future of molecular string representation","volume":"3","author":"Krenn","year":"2022","journal-title":"Patterns"},{"key":"2024102209543214300_btae574-B17","author":"Li","year":"2022"},{"key":"2024102209543214300_btae574-B18","author":"Li","year":"2022"},{"key":"2024102209543214300_btae574-B19","author":"Liu"},{"key":"2024102209543214300_btae574-B20","doi-asserted-by":"publisher","first-page":"btad371","DOI":"10.1093\/bioinformatics\/btad371","article-title":"3d graph contrastive learning for molecular property prediction","volume":"39","author":"Moon","year":"2023","journal-title":"Bioinformatics"},{"key":"2024102209543214300_btae574-B21","first-page":"12559","article-title":"Self-supervised graph transformer on large-scale molecular data","volume":"33","author":"Rong","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"2024102209543214300_btae574-B22","author":"St\u00e4rk","year":"2022"},{"key":"2024102209543214300_btae574-B23","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J Mach Learn Res"},{"key":"2024102209543214300_btae574-B24","article-title":"Attention is all you need","author":"Vaswani","year":"2017","journal-title":"In: 31st Conference on Neural Information Processing Systems (NIPS 2017), Vol. 30, Long Beach, CA, USA,"},{"key":"2024102209543214300_btae574-B25","doi-asserted-by":"publisher","first-page":"btad258","DOI":"10.1093\/bioinformatics\/btad258","article-title":"Molecular property prediction by contrastive learning with attention-guided positive sample selection","volume":"39","author":"Wang","year":"2023","journal-title":"Bioinformatics"},{"key":"2024102209543214300_btae574-B26","author":"Wang","year":"2023"},{"key":"2024102209543214300_btae574-B27","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1038\/s42256-022-00447-x","article-title":"Molecular contrastive learning of representations via graph neural networks","volume":"4","author":"Wang","year":"2022","journal-title":"Nat Mach Intell"},{"key":"2024102209543214300_btae574-B28","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1021\/ci00057a005","article-title":"Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules","volume":"28","author":"Weininger","year":"1988","journal-title":"J Chem Inf Comput Sci"},{"key":"2024102209543214300_btae574-B29","doi-asserted-by":"publisher","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":"2024102209543214300_btae574-B30","author":"Xu"},{"key":"2024102209543214300_btae574-B31","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1038\/s42004-023-00825-5","article-title":"Hierarchical molecular graph self-supervised learning for property prediction","volume":"6","author":"Zang","year":"2023","journal-title":"Commun Chem"},{"key":"2024102209543214300_btae574-B32","author":"Zhou","year":"2023"},{"key":"2024102209543214300_btae574-B33","first-page":"2626","author":"Zhu","year":"2022"}],"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btae574\/59721591\/btae574.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/10\/btae574\/59940234\/btae574.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article-pdf\/40\/10\/btae574\/59940234\/btae574.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,22]],"date-time":"2024-10-22T09:54:54Z","timestamp":1729590894000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/article\/doi\/10.1093\/bioinformatics\/btae574\/7818417"}},"subtitle":[],"editor":[{"given":"Jonathan","family":"Wren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2024,10,1]]},"references-count":32,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,10,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btae574","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2024,10]]},"published":{"date-parts":[[2024,10,1]]},"article-number":"btae574"}}