{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,2]],"date-time":"2026-03-02T22:06:32Z","timestamp":1772489192681,"version":"3.50.1"},"reference-count":42,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["61672329"],"award-info":[{"award-number":["61672329"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Science Foundation of China","doi-asserted-by":"publisher","award":["62072290"],"award-info":[{"award-number":["62072290"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Provincial Project of Education Scientific Plan","award":["SDYY18058"],"award-info":[{"award-number":["SDYY18058"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,1,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Molecular property prediction is a significant requirement in AI-driven drug design and discovery, aiming to predict the molecular property information (e.g. toxicity) based on the mined biomolecular knowledge. Although graph neural networks have been proven powerful in predicting molecular property, unbalanced labeled data and poor generalization capability for new-synthesized molecules are always key issues that hinder further improvement of molecular encoding performance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We propose a novel self-supervised representation learning scheme based on a Cascaded Attention Network and Graph Contrastive Learning (CasANGCL). We design a new graph network variant, designated as cascaded attention network, to encode local\u2013global molecular representations. We construct a two-stage contrast predictor framework to tackle the label imbalance problem of training molecular samples, which is an integrated end-to-end learning scheme. Moreover, we utilize the information-flow scheme for training our network, which explicitly captures the edge information in the node\/graph representations and obtains more fine-grained knowledge. Our model achieves an 81.9% ROC-AUC average performance on 661 tasks from seven challenging benchmarks, showing better portability and generalizations. Further visualization studies indicate our model\u2019s better representation capacity and provide interpretability.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bib\/bbac566","type":"journal-article","created":{"date-parts":[[2023,1,2]],"date-time":"2023-01-02T14:37:02Z","timestamp":1672670222000},"source":"Crossref","is-referenced-by-count":24,"title":["CasANGCL: pre-training and fine-tuning model based on cascaded attention network and graph contrastive learning for molecular property prediction"],"prefix":"10.1093","volume":"24","author":[{"given":"Zixi","family":"Zheng","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University ,Jinan 250358, China"}]},{"given":"Yanyan","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University ,Jinan 250358, China"}]},{"given":"Hong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University ,Jinan 250358, China"}]},{"given":"Shengpeng","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University ,Jinan 250358, China"}]},{"given":"Tianyu","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University ,Jinan 250358, China"}]},{"given":"Cheng","family":"Liang","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shandong Normal University ,Jinan 250358, China"},{"name":"Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology, Shandong Normal University , Jinan 250358 , China"}]}],"member":"286","published-online":{"date-parts":[[2023,1,2]]},"reference":[{"key":"2023011917081437200_ref1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ddtec.2020.11.009","article-title":"A compact review of molecular property prediction with graph neural networks","volume":"37","author":"Wieder","year":"2020","journal-title":"Drug Discov Today Technol"},{"issue":"6","key":"2023011917081437200_ref2","doi-asserted-by":"crossref","first-page":"05","DOI":"10.1093\/bib\/bbab152","article-title":"Mg-bert: leveraging unsupervised atomic representation learning for molecular property prediction","volume":"22","author":"Zhang","year":"2021","journal-title":"Brief Bioinform"},{"key":"2023011917081437200_ref3","first-page":"15870","volume-title":"Advances in Neural Information Processing Systems","author":"Zhang","year":"2021"},{"issue":"15","key":"2023011917081437200_ref4","doi-asserted-by":"crossref","first-page":"3411","DOI":"10.1016\/S0009-2509(98)00489-8","article-title":"Modeling surface kinetics with first-principles-based molecular simulation","volume":"54","author":"Hansen","year":"1999","journal-title":"Chem Eng Sci"},{"issue":"2","key":"2023011917081437200_ref5","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1021\/ci00062a008","article-title":"Smiles. 2. Algorithm for generation of unique smiles notation","volume":"29","author":"Weininger","year":"1989","journal-title":"J Chem Inf Comput Sci"},{"key":"2023011917081437200_ref6","volume-title":"Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics."},{"key":"2023011917081437200_ref7","volume-title":"Advances in Neural Information Processing Systems","year":"2019"},{"key":"2023011917081437200_ref8","volume-title":"Proceedings of the 34th International Conference on Machine Learning"},{"issue":"8","key":"2023011917081437200_ref9","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":"2023011917081437200_ref10","volume-title":"Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence"},{"key":"2023011917081437200_ref11","volume-title":"Advances in Neural Information Processing Systems"},{"key":"2023011917081437200_ref12","volume-title":"The Eighth Internatinal Conference on Learning Representations"},{"key":"2023011917081437200_ref13","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.108274","article-title":"Bi-clkt: bi-graph contrastive learning based knowledge tracing","volume":"241","author":"Song","year":"2022","journal-title":"Knowledge-Based Systems"},{"key":"2023011917081437200_ref14","doi-asserted-by":"crossref","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":"2023011917081437200_ref15","volume-title":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining KDD\u2019 21"},{"key":"2023011917081437200_ref16","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"2023011917081437200_ref17","volume":"4"},{"key":"2023011917081437200_ref18","first-page":"18661","article-title":"Supervised contrastive learning","volume":"33","author":"Khosla","year":"2020","journal-title":"In Advances in Neural Information Processing Systems"},{"key":"2023011917081437200_ref19","volume-title":"International Conference for Learning Representation"},{"key":"2023011917081437200_ref20","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1109\/TASLP.2020.3042009","article-title":"Investigating typed syntactic dependencies for targeted sentiment classification using graph attention neural network","volume":"29","author":"Bai","year":"2021","journal-title":"IEEE\/ACM Transactions on Audio, Speech, and Language Processing"},{"key":"2023011917081437200_ref21","first-page":"5998","article-title":"Attention is all you need","volume":"30","author":"Aidan","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"2023011917081437200_ref22","volume-title":"Proceedings of the 37th International Conference on Machine Learning"},{"key":"2023011917081437200_ref23","volume-title":"Advances in Neural Information Processing Systems"},{"key":"2023011917081437200_ref24","volume":"72"},{"issue":"10","key":"2023011917081437200_ref25","doi-asserted-by":"crossref","first-page":"1936","DOI":"10.1021\/acs.jcim.6b00290","volume":"56","author":"Subramanian","year":"2016","journal-title":"J Chem Inf Model"},{"issue":"6","key":"2023011917081437200_ref26","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.1021\/ci300124c","article-title":"A bayesian approach to in silico blood-brain barrier penetration modeling","volume":"52","author":"Martins","year":"2012","journal-title":"J Chem Inf Model"},{"issue":"10","key":"2023011917081437200_ref27","doi-asserted-by":"crossref","first-page":"1294","DOI":"10.1016\/j.chembiol.2016.07.023","article-title":"A data-driven approach to predicting successes and failures of clinical trials","volume":"23","author":"Gayvert","year":"2016","journal-title":"Cell Chemical Biology"},{"key":"2023011917081437200_ref28"},{"issue":"D1","key":"2023011917081437200_ref29","doi-asserted-by":"crossref","first-page":"D1075","DOI":"10.1093\/nar\/gkv1075","article-title":"The SIDER database of drugs and side effects","volume":"44","author":"Kuhn","year":"2015","journal-title":"Nucleic Acids Res"},{"key":"2023011917081437200_ref30","volume":"8"},{"issue":"8","key":"2023011917081437200_ref31","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":"2023011917081437200_ref32"},{"key":"2023011917081437200_ref33","journal-title":"Materials Science and Biology"},{"issue":"2","key":"2023011917081437200_ref34","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1039\/C7SC02664A","article-title":"Moleculenet: a benchmark for molecular machine learning","volume":"9","author":"Feinberg","year":"2018","journal-title":"Chem Sci"},{"key":"2023011917081437200_ref35"},{"key":"2023011917081437200_ref36"},{"issue":"8","key":"2023011917081437200_ref37","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":"2023011917081437200_ref38","volume":"30","journal-title":"Advances in neural information processing systems"},{"key":"2023011917081437200_ref39","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1609\/aaai.v33i01.33011052","article-title":"Molecular property prediction: a multilevel quantum interactions modeling perspective","volume":"33","author":"Lu","year":"2019","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"2023011917081437200_ref40"},{"key":"2023011917081437200_ref41","journal-title":"Front Mol Biosci"},{"key":"2023011917081437200_ref42","volume":"23","journal-title":"Brief Bioinform"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/1\/bbac566\/48781959\/bbac566.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/24\/1\/bbac566\/48781959\/bbac566.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T17:13:29Z","timestamp":1674148409000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbac566\/6966532"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":42,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023,1,19]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbac566","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,1]]},"published":{"date-parts":[[2023,1]]},"article-number":"bbac566"}}