{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T04:34:11Z","timestamp":1777350851468,"version":"3.51.4"},"reference-count":47,"publisher":"American Chemical Society (ACS)","issue":"19","license":[{"start":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T00:00:00Z","timestamp":1663718400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T00:00:00Z","timestamp":1663718400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T00:00:00Z","timestamp":1663718400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81903438"],"award-info":[{"award-number":["81903438"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004731","name":"Natural Science Foundation of Zhejiang Province","doi-asserted-by":"publisher","award":["LD22H300004"],"award-info":[{"award-number":["LD22H300004"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Chem. Inf. Model."],"published-print":{"date-parts":[[2022,10,10]]},"DOI":"10.1021\/acs.jcim.2c00588","type":"journal-article","created":{"date-parts":[[2022,9,21]],"date-time":"2022-09-21T07:10:27Z","timestamp":1663744227000},"page":"4579-4590","source":"Crossref","is-referenced-by-count":6,"title":["Self-Supervised Molecular Pretraining Strategy for Low-Resource Reaction Prediction Scenarios"],"prefix":"10.1021","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3535-1081","authenticated-orcid":true,"given":"Zhipeng","family":"Wu","sequence":"first","affiliation":[{"name":"Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, P. R. China"}]},{"given":"Xiang","family":"Cai","sequence":"additional","affiliation":[{"name":"PyWise Biotech, Suzhou 215000, P. R. China"}]},{"given":"Chengyun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, P. R. China"}]},{"given":"Haoran","family":"Qiao","sequence":"additional","affiliation":[{"name":"College of Mathematics and Physics, Shanghai University of Electric Power, Shanghai 201203, P. R. China"}]},{"given":"Yejian","family":"Wu","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, P. R. China"}]},{"given":"Yun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6071-832X","authenticated-orcid":true,"given":"Xinqiao","family":"Wang","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, P. R. China"}]},{"given":"Haiying","family":"Xie","sequence":"additional","affiliation":[{"name":"PUROTON Gene Medical Institute Co., Ltd., Chongqing 400700, P. R. China"}]},{"given":"Feng","family":"Luo","sequence":"additional","affiliation":[{"name":"PUROTON Gene Medical Institute Co., Ltd., Chongqing 400700, P. R. China"}]},{"given":"Hongliang","family":"Duan","sequence":"additional","affiliation":[{"name":"Artificial Intelligence Aided Drug Discovery Institute, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou 310014, P. R. China"}]}],"member":"316","published-online":{"date-parts":[[2022,9,21]]},"reference":[{"key":"ref1\/cit1","doi-asserted-by":"publisher","DOI":"10.1038\/s41557-018-0021-z"},{"key":"ref2\/cit2","doi-asserted-by":"crossref","unstructured":"Schwaller, P.; Laino, T. In\n                      ACS Symposium Series 1326\n                      , Data-Driven Learning Systems for Chem-ical Reaction Prediction: An Analysis of Recent Approaches. In Ma-chine Learning in Chemistry: Data-Driven Algorithms, Learning Sys-tems, and Predictions, Pyzer-Knapp, E. O.; Laino, T., Eds. American Chemical Society: Washington, DC, 2019; Chapter 4 pp, 61\u201379.","DOI":"10.1021\/bk-2019-1326.ch004"},{"key":"ref3\/cit3","doi-asserted-by":"publisher","DOI":"10.1016\/j.coche.2021.100749"},{"key":"ref4\/cit4","doi-asserted-by":"publisher","DOI":"10.1351\/pac199062101921"},{"key":"ref5\/cit5","doi-asserted-by":"publisher","DOI":"10.1021\/ci00023a005"},{"key":"ref6\/cit6","doi-asserted-by":"publisher","DOI":"10.1002\/chem.201604556"},{"key":"ref7\/cit7","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00064"},{"key":"ref8\/cit8","doi-asserted-by":"publisher","DOI":"10.1016\/j.drudis.2018.02.014"},{"key":"ref9\/cit9","doi-asserted-by":"publisher","DOI":"10.1021\/ar9501986"},{"key":"ref10\/cit10","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.5b00830"},{"key":"ref11\/cit11","doi-asserted-by":"publisher","DOI":"10.2533\/chimia.2019.997"},{"key":"ref12\/cit12","unstructured":"Nam, J.; Kim, J. Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions. 2016, arXiv:1612.09529. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/1612.09529."},{"key":"ref13\/cit13","first-page":"2607","author":"Jin W.","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref14\/cit14","doi-asserted-by":"publisher","DOI":"10.1126\/science.aar5169"},{"key":"ref15\/cit15","doi-asserted-by":"publisher","DOI":"10.1039\/C9CC05122H"},{"key":"ref16\/cit16","doi-asserted-by":"publisher","DOI":"10.1038\/s41570-019-0124-0"},{"key":"ref17\/cit17","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.9b00576"},{"key":"ref18\/cit18","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-19266-y"},{"key":"ref19\/cit19","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-18671-7"},{"key":"ref20\/cit20","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.9b02120"},{"key":"ref21\/cit21","first-page":"6000","author":"Vaswani A.","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref22\/cit22","unstructured":"Lowe, D. M. Extraction of Chemical Structures and Reactions from the Literature. Ph.D. Thesis, University of Cambridge, 2012."},{"key":"ref23\/cit23","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2009.191"},{"key":"ref24\/cit24","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.9b02147"},{"key":"ref25\/cit25","doi-asserted-by":"publisher","DOI":"10.1039\/D0CC02657C"},{"key":"ref26\/cit26","doi-asserted-by":"publisher","DOI":"10.1039\/D0QO01636E"},{"key":"ref27\/cit27","unstructured":"Chithrananda, S.; Grand, G.; Ramsundar, B. ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction. 2020, arXiv:2010.09885. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/2010.09885."},{"key":"ref28\/cit28","unstructured":"Fabian, B.; Edlich, T.; Gaspar, H.; Segler, M.; Meyers, J.; Fiscato, M.; Ahmed, M. Molecular Representation Learning with Language Models and Domain-Relevant Auxiliary Tasks. 2020, arXiv:2011.13230. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/2011.13230."},{"key":"ref29\/cit29","doi-asserted-by":"publisher","DOI":"10.1016\/j.scib.2022.01.029"},{"key":"ref30\/cit30","unstructured":"Song, K.; Tan, X.; Qin, T.; Lu, J.; Liu, T. MASS: Masked Sequence to Sequence Pre-Training for Language Generation. 2019, arXiv:1905.02450. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/1905.02450."},{"key":"ref31\/cit31","doi-asserted-by":"publisher","DOI":"10.1021\/cr030011l"},{"key":"ref32\/cit32","doi-asserted-by":"crossref","first-page":"5518","DOI":"10.1021\/ja01022a034","volume":"90","author":"Heck R. F.","year":"1968","journal-title":"J. Am. Chem. Soc."},{"key":"ref33\/cit33","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00303"},{"key":"ref34\/cit34","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.5b00559"},{"key":"ref35\/cit35","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gky1075"},{"key":"ref36\/cit36","unstructured":"Reaxys. https:\/\/www.reaxys.com\/."},{"key":"ref37\/cit37","doi-asserted-by":"publisher","DOI":"10.1021\/ci00057a005"},{"key":"ref38\/cit38","unstructured":"Jastrzebski, S.; Lesniak, D.; Czarnecki, W. M. Learning to Smile(s). 2016, arXiv:1602.06289. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/1602.06289."},{"key":"ref39\/cit39","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/B978-0-12-809633-8.20349-X","volume-title":"Encyclopedia of Bioinformatics and Computational Biology","author":"Berrar D.","year":"2019"},{"key":"ref40\/cit40","doi-asserted-by":"crossref","unstructured":"Siddhant, A.; Bapna, A.; Cao, Y.; Firat, O.; Chen, M.; Kudugunta, S.; Arivazhagan, N.; Wu, Y. Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation. 2020, arXiv:2005.04816. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/2005.04816.","DOI":"10.18653\/v1\/2020.acl-main.252"},{"key":"ref41\/cit41","unstructured":"Devlin, J.; Chang, M.W.; Lee, K.; Toutanova, K. Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding. 2018, arXiv:1810.04805. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/1810.04805."},{"key":"ref42\/cit42","unstructured":"Kalyan, K. S.; Rajasekharan, A.; Angeetha, S. Ammus\n                      :\n                      A Survey of Transformer-Based Pre-Trained Models in Natural Language Processing. 2021, arXiv:2108.05542. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/2108.05542."},{"key":"ref43\/cit43","doi-asserted-by":"crossref","unstructured":"Gillioz, A.; Casas, C.; Mugellini, E.; Khaled, O. A. In\n                      Overview of the Transformer-Based Models For Nlp Tasks\n                      , Proceedings of the 2020 Federated Conference on Computer Science and Information Systems; IEEE: Sofia, Bulgaria, 2020; pp 179\u2013183.","DOI":"10.15439\/2020F20"},{"key":"ref44\/cit44","unstructured":"Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; Uszkoreit, J.; Houlsby, N. An Image is Worth 16 \u00d7 16 Words: Transformers for Image Recognition at Scale. 2020, arXiv:2010.11929. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/2010.11929."},{"key":"ref45\/cit45","doi-asserted-by":"crossref","unstructured":"He, K.; Zhang, X.; Ren, S.; Sun, J. In\n                      Deep Residual Learning for Image Recognition\n                      , Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; IEEE: Las Vegas, NV, USA, 2016; pp 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref46\/cit46","unstructured":"Gehring, J.; Auli, M.; Grangier, D.; Yarats, D.; Dauphin, Y. N. In\n                      Convolutional Sequence to Sequence Learning\n                      , Proceedings of the 34th International Conference on Machine Learning; PMLR, 2017; pp 1243\u20131252."},{"key":"ref47\/cit47","unstructured":"Mielke, S. J.; Alyafeai, Z.; Salesky, E.; Raffel, C.; Dey, M.; Gall\u00e9, M.; Raja, A.; Si, C.; Lee, W. Y.; Sagot, B.; Tan, S. Between Words and Characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLP. 2021, arXiv:2112.10508. arXiv.org e-Print archive. https:\/\/arxiv.org\/abs\/2112.10508."}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.2c00588","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.2c00588","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T06:49:56Z","timestamp":1682491796000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.jcim.2c00588"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,21]]},"references-count":47,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2022,10,10]]}},"alternative-id":["10.1021\/acs.jcim.2c00588"],"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.2c00588","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv-2021-fxvwg-v2","asserted-by":"object"}]},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,21]]}}}