{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,20]],"date-time":"2026-02-20T02:53:32Z","timestamp":1771556012957,"version":"3.50.1"},"reference-count":209,"publisher":"American Chemical Society (ACS)","issue":"8","license":[{"start":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T00:00:00Z","timestamp":1710460800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC2303700"],"award-info":[{"award-number":["2022YFC2303700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFF1203003"],"award-info":[{"award-number":["2022YFF1203003"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100011247","name":"State Key Laboratory of Pharmaceutical Biotechnology","doi-asserted-by":"publisher","award":["KF-202304"],"award-info":[{"award-number":["KF-202304"]}],"id":[{"id":"10.13039\/501100011247","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Peking University Health Science-StoneWise Technology Joint Laboratory Project","award":["L202107"],"award-info":[{"award-number":["L202107"]}]},{"name":"Beijing AI Health Cultivation Project","award":["Z221100003522022"],"award-info":[{"award-number":["Z221100003522022"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Chem. Inf. Model."],"published-print":{"date-parts":[[2024,4,22]]},"DOI":"10.1021\/acs.jcim.4c00004","type":"journal-article","created":{"date-parts":[[2024,3,15]],"date-time":"2024-03-15T17:16:03Z","timestamp":1710522963000},"page":"2955-2970","source":"Crossref","is-referenced-by-count":22,"title":["Exploring Chemical Reaction Space with Machine Learning Models: Representation and Feature Perspective"],"prefix":"10.1021","volume":"64","author":[{"given":"Yuheng","family":"Ding","sequence":"first","affiliation":[{"name":"Department of Pharmaceutical Science, Peking University, Beijing 100191, China"}]},{"given":"Bo","family":"Qiang","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Science, Peking University, Beijing 100191, China"}]},{"given":"Qixuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Science, Peking University, Beijing 100191, China"}]},{"given":"Yiqiao","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Science, Peking University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7362-9497","authenticated-orcid":true,"given":"Liangren","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Science, Peking University, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8993-4015","authenticated-orcid":true,"given":"Zhenming","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Science, Peking University, Beijing 100191, China"}]}],"member":"316","published-online":{"date-parts":[[2024,3,15]]},"reference":[{"key":"ref1\/cit1","doi-asserted-by":"publisher","DOI":"10.1021\/ja00973a068"},{"key":"ref2\/cit2","doi-asserted-by":"publisher","DOI":"10.1042\/bj0221341"},{"key":"ref3\/cit3","doi-asserted-by":"publisher","DOI":"10.1002\/app.50376"},{"key":"ref4\/cit4","doi-asserted-by":"publisher","DOI":"10.1021\/ja908911n"},{"key":"ref5\/cit5","doi-asserted-by":"publisher","DOI":"10.1021\/jacs.3c08373"},{"key":"ref6\/cit6","doi-asserted-by":"publisher","DOI":"10.1126\/science.aay9501"},{"key":"ref7\/cit7","doi-asserted-by":"publisher","DOI":"10.1021\/ja00341a054"},{"key":"ref8\/cit8","doi-asserted-by":"publisher","DOI":"10.1021\/ja00107a006"},{"key":"ref9\/cit9","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-29407-6_5"},{"key":"ref10\/cit10","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref11\/cit11","doi-asserted-by":"publisher","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.  arXiv 2021.10.48550\/arXiv.2010.11929","DOI":"10.48550\/arXiv.2010.11929"},{"key":"ref12\/cit12","doi-asserted-by":"publisher","unstructured":"Devlin, J.; Chang, M.W.; Lee, K.; Toutanova, K. BERT: Pre-Training of Deep Bidirectional Transformers for Language Understanding.  arXiv 201810.48550\/arXiv.1810.04805.","DOI":"10.48550\/arXiv.1810.04805"},{"key":"ref13\/cit13","first-page":"1877","volume":"33","author":"Brown T.","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref14\/cit14","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"ref15\/cit15","first-page":"27730","volume":"35","author":"Ouyang L.","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref16\/cit16","unstructured":"Radford, A.; Narasimhan, K.; Salimans, T.; Sutskever, I.;  Improving Language Understanding by Generative Pre-Training.  OpenAI. 2018."},{"key":"ref17\/cit17","first-page":"9","volume":"1","author":"Radford A.","year":"2019","journal-title":"OpenAI blog"},{"key":"ref18\/cit18","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1923-7"},{"key":"ref19\/cit19","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03819-2"},{"key":"ref20\/cit20","first-page":"28277","volume":"202","author":"Qiang B.","year":"2023","journal-title":"International Conference on Machine Learning"},{"key":"ref21\/cit21","doi-asserted-by":"publisher","unstructured":"Xu, M.; Yu, L.; Song, Y.; Shi, C.; Ermon, S.; Tang, J. Geodiff: a Geometric Diffusion Model for Molecular Conformation Generation arXiv, 2022.10.48550\/arXiv.2203.02923","DOI":"10.48550\/arXiv.2203.02923"},{"key":"ref23\/cit23","doi-asserted-by":"publisher","DOI":"10.1126\/science.ade2574"},{"key":"ref24\/cit24","doi-asserted-by":"publisher","unstructured":"Zhou, G.; Gao, Z.; Ding, Q.; Zheng, H.; Xu, H.; Wei, Z.; Zhang, L.; Ke, G. Uni-Mol: A Universal 3D Molecular Representation Learning Framework. 2023.10.26434\/chemrxiv-2022-jjm0j-v4","DOI":"10.26434\/chemrxiv-2022-jjm0j-v4"},{"key":"ref25\/cit25","doi-asserted-by":"publisher","unstructured":"Rong, Y.; Bian, Y.; Xu, T.; Xie, W.; Wei, Y.; Huang, W.; Huang, J. Self-Supervised Graph Transformer on Large-Scale Molecular Data. 2020.10.48550\/arXiv.2007.02835","DOI":"10.48550\/arXiv.2007.02835"},{"key":"ref26\/cit26","doi-asserted-by":"publisher","unstructured":"Gao, B.; Qiang, B.; Tan, H.; Ren, M.; Jia, Y.; Lu, M.; Liu, J.; Ma, W.; Lan, Y. DrugCLIP: Contrastive Protein-Molecule Representation Learning for Virtual Screening.  arXiv 2023.10.48550\/arXiv.2310.06367","DOI":"10.48550\/arXiv.2310.06367"},{"key":"ref27\/cit27","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00266"},{"key":"ref28\/cit28","doi-asserted-by":"publisher","DOI":"10.1038\/s41570-019-0124-0"},{"key":"ref29\/cit29","doi-asserted-by":"publisher","DOI":"10.1039\/D1DD00006C"},{"key":"ref30\/cit30","doi-asserted-by":"publisher","DOI":"10.1126\/science.3838594"},{"key":"ref31\/cit31","doi-asserted-by":"publisher","DOI":"10.1351\/pac196714010019"},{"key":"ref32\/cit32","doi-asserted-by":"publisher","DOI":"10.1021\/acs.accounts.8b00087"},{"key":"ref33\/cit33","unstructured":"Lowe, D. M. Extraction of Chemical Structures and Reactions from the Literature, Ph.D. Thesis, University of Cambridge, 2012."},{"key":"ref34\/cit34","doi-asserted-by":"publisher","DOI":"10.1039\/C8SC02339E"},{"key":"ref35\/cit35","volume":"30","author":"Jin W.","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref36\/cit36","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.6b00564"},{"key":"ref37\/cit37","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-020-00284-w"},{"key":"ref38\/cit38","doi-asserted-by":"publisher","DOI":"10.5195\/jmla.2018.515"},{"issue":"12","key":"ref39\/cit39","doi-asserted-by":"crossref","first-page":"2897","DOI":"10.1021\/ci900437n","volume":"49","author":"Goodman J.","year":"2009","journal-title":"J. Chem. Inf. Model."},{"key":"ref40\/cit40","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.8b00357"},{"key":"ref41\/cit41","doi-asserted-by":"publisher","DOI":"10.1021\/jacs.1c09820"},{"key":"ref42\/cit42","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-021-03213-y"},{"key":"ref43\/cit43","doi-asserted-by":"publisher","DOI":"10.1126\/science.aar5169"},{"key":"ref44\/cit44","doi-asserted-by":"publisher","DOI":"10.1021\/co400012m"},{"key":"ref45\/cit45","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/abc81d"},{"key":"ref46\/cit46","doi-asserted-by":"publisher","DOI":"10.1039\/D2SC06041H"},{"key":"ref47\/cit47","doi-asserted-by":"publisher","DOI":"10.1039\/C9RE00086K"},{"key":"ref48\/cit48","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-019-1540-5"},{"key":"ref49\/cit49","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpca.3c01430"},{"key":"ref50\/cit50","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-021-21895-w"},{"key":"ref51\/cit51","unstructured":"Friedel, C.; Crafts, J. M. Sur Une Methode Generale Nouvelle De Synthese D\u2019Hydrocarbures, D\u2019acetones, etc.; 1877."},{"key":"ref52\/cit52","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.3c00372"},{"key":"ref53\/cit53","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00319-w"},{"key":"ref54\/cit54","doi-asserted-by":"publisher","DOI":"10.1002\/wcms.1604"},{"key":"ref55\/cit55","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.3c01163"},{"key":"ref56\/cit56","volume-title":"Feature Engineering for Machine Learning and Data Analytics","author":"Dong G.","year":"2018"},{"key":"ref57\/cit57","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-010-0001-0"},{"key":"ref58\/cit58","doi-asserted-by":"publisher","unstructured":"Mikolov, T.; Chen, K.; Corrado, G.; Dean, J. Efficient Estimation of Word Representations in Vector Space.  arXiv 2013.10.48550\/arXiv.1301.3781","DOI":"10.48550\/arXiv.1301.3781"},{"key":"ref59\/cit59","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-023-00764-9"},{"key":"ref60\/cit60","doi-asserted-by":"publisher","DOI":"10.1039\/D1SC06515G"},{"key":"ref61\/cit61","doi-asserted-by":"publisher","DOI":"10.1021\/ci00062a008"},{"key":"ref62\/cit62","doi-asserted-by":"publisher","unstructured":"Krenn, M.; H\u00e4se, F.; Nigam, A.; Friederich, P.; Aspuru-Guzik, A. Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation arXiv10.48550\/arXiv.1905.13741.","DOI":"10.48550\/arXiv.1905.13741"},{"key":"ref63\/cit63","doi-asserted-by":"publisher","unstructured":"Fang, Y.; Zhang, N.; Chen, Z.; Guo, L.; Fan, X.; Chen, H. Domain-Agnostic Molecular Generation with Self-Feedback.  arXiv 2023.10.48550\/arXiv.2301.11259","DOI":"10.48550\/arXiv.2301.11259"},{"key":"ref64\/cit64","first-page":"3104","volume":"27","author":"Sutskever I.","year":"2014","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref65\/cit65","first-page":"6000","volume":"30","author":"Vaswani A.","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref66\/cit66","doi-asserted-by":"publisher","unstructured":"Bahdanau, D.; Cho, K.; Bengio, Y. Neural Machine Translation by Jointly Learning to Align and Translate.  arXiv 2014.10.48550\/arXiv.1409.0473","DOI":"10.48550\/arXiv.1409.0473"},{"key":"ref67\/cit67","doi-asserted-by":"publisher","unstructured":"Luong, M.T.; Pham, H.; Manning, C. D. Effective Approaches to Attention-Based Neural Machine Translation.  arXiv 201510.48550\/arXiv.1508.04025.","DOI":"10.48550\/arXiv.1508.04025"},{"key":"ref68\/cit68","doi-asserted-by":"publisher","unstructured":"Nam, J.; Kim, J. Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions.  arXiv 2016.10.48550\/arXiv.1612.09529","DOI":"10.48550\/arXiv.1612.09529"},{"key":"ref69\/cit69","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.9b00576"},{"key":"ref70\/cit70","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30493-5_78"},{"key":"ref71\/cit71","doi-asserted-by":"publisher","DOI":"10.1039\/C9RA08535A"},{"key":"ref72\/cit72","doi-asserted-by":"publisher","DOI":"10.1186\/s40537-021-00492-0"},{"key":"ref73\/cit73","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-19266-y"},{"key":"ref74\/cit74","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/ac3ffb"},{"key":"ref75\/cit75","doi-asserted-by":"publisher","unstructured":"Tang, Y.; Tran, C.; Li, X.; Chen, P.J.; Goyal, N.; Chaudhary, V.; Gu, J.; Fan, A. Multilingual Translation with Extensible Multilingual Pretraining and Finetuning.  arXiv 2020.10.48550\/arXiv.2008.00401","DOI":"10.48550\/arXiv.2008.00401"},{"key":"ref76\/cit76","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-28536-w"},{"key":"ref77\/cit77","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-18671-7"},{"key":"ref78\/cit78","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-37969-w"},{"key":"ref79\/cit79","unstructured":"Shi, C.; Xu, M.; Guo, H.; Zhang, M.; Tang, J. A Graph to Graphs Framework for Retrosynthesis Prediction. 2021."},{"key":"ref80\/cit80","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abe4166"},{"key":"ref81\/cit81","doi-asserted-by":"publisher","DOI":"10.1039\/D2SC02763A"},{"key":"ref82\/cit82","doi-asserted-by":"publisher","DOI":"10.1021\/ci5006614"},{"key":"ref83\/cit83","doi-asserted-by":"publisher","DOI":"10.1002\/chem.201605499"},{"key":"ref84\/cit84","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.6b00219"},{"key":"ref85\/cit85","doi-asserted-by":"publisher","DOI":"10.1021\/ci100050t"},{"key":"ref86\/cit86","doi-asserted-by":"publisher","DOI":"10.1021\/ci010132r"},{"key":"ref88\/cit88","doi-asserted-by":"publisher","DOI":"10.1186\/1752-153X-2-3"},{"key":"ref89\/cit89","doi-asserted-by":"publisher","DOI":"10.1021\/ci200199u"},{"key":"ref90\/cit90","doi-asserted-by":"publisher","DOI":"10.1007\/s11030-021-10223-5"},{"key":"ref91\/cit91","doi-asserted-by":"publisher","DOI":"10.1021\/ci00046a002"},{"key":"ref92\/cit92","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1521-3773(19991004)38:19<2894::AID-ANIE2894>3.0.CO;2-F"},{"key":"ref93\/cit93","doi-asserted-by":"publisher","DOI":"10.1021\/ci00054a008"},{"key":"ref94\/cit94","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c01065"},{"key":"ref95\/cit95","doi-asserted-by":"publisher","DOI":"10.1109\/TCIAIG.2012.2186810"},{"key":"ref96\/cit96","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.9b00055"},{"key":"ref97\/cit97","doi-asserted-by":"publisher","unstructured":"Gao, W.; Mercado, R.; Coley, C. W. Amortized Tree Generation for Bottom-Up Synthesis Planning and Synthesizable Molecular Design.  arXiv 2021.10.48550\/arXiv.2110.06389","DOI":"10.48550\/arXiv.2110.06389"},{"key":"ref98\/cit98","doi-asserted-by":"publisher","DOI":"10.3390\/ijms23020811"},{"key":"ref99\/cit99","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymeth.2014.08.005"},{"key":"ref100\/cit100","doi-asserted-by":"publisher","DOI":"10.1016\/j.chempr.2020.02.017"},{"key":"ref101\/cit101","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref102\/cit102","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-56773-5"},{"key":"ref103\/cit103","first-page":"6790","volume":"34","author":"Gasteiger J.","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref104\/cit104","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00058"},{"key":"ref105\/cit105","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403117"},{"key":"ref106\/cit106","first-page":"8818","volume":"119","author":"Shi C.","year":"2020","journal-title":"International Conference on Machine Learning"},{"key":"ref107\/cit107","first-page":"11248","volume":"33","author":"Yan C.","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref108\/cit108","first-page":"9405","volume":"34","author":"Somnath V. R.","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref109\/cit109","doi-asserted-by":"publisher","DOI":"10.1021\/jacsau.1c00246"},{"key":"ref110\/cit110","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00064"},{"key":"ref111\/cit111","doi-asserted-by":"publisher","unstructured":"Xie, S.; Yan, R.; Guo, J.; Xia, Y.; Wu, L.; Qin, T. Retrosynthesis Prediction with Local Template Retrieval.  arXiv 2023.10.48550\/arXiv.2306.04123","DOI":"10.48550\/arXiv.2306.04123"},{"key":"ref112\/cit112","doi-asserted-by":"publisher","unstructured":"Bradshaw, J.; Kusner, M. J.; Paige, B.; Segler, M. H.; Hern\u00e1ndez-Lobato, J. M. A Generative Model for Electron Paths.  arXiv 2018.10.48550\/arXiv.1805.10970","DOI":"10.48550\/arXiv.1805.10970"},{"key":"ref113\/cit113","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00526-z"},{"key":"ref114\/cit114","first-page":"904","author":"Bi H.","year":"2021","journal-title":"International Conference on Machine Learning"},{"key":"ref115\/cit115","doi-asserted-by":"publisher","DOI":"10.1021\/ci00017a011"},{"key":"ref116\/cit116","doi-asserted-by":"publisher","unstructured":"Asadi, K.; Misra, D.; Kim, S.; Littman, M. L. Combating the Compounding-Error Problem with a Multi-Step Model.  arXiv 2019.10.48550\/arXiv.1905.13320","DOI":"10.48550\/arXiv.1905.13320"},{"key":"ref117\/cit117","doi-asserted-by":"publisher","DOI":"10.3389\/fenrg.2021.723319"},{"key":"ref118\/cit118","first-page":"1608","author":"Chen B.","year":"2020","journal-title":"Proceedings of the 37Th International Conference on Machine Learning"},{"key":"ref119\/cit119","doi-asserted-by":"publisher","DOI":"10.1038\/s42004-023-00911-8"},{"key":"ref120\/cit120","doi-asserted-by":"publisher","unstructured":"Igashov, I.; Schneuing, A.; Segler, M.; Bronstein, M.; Correia, B. RetroBridge: Modeling Retrosynthesis with Markov Bridges.  arXiv 2023.10.48550\/arXiv.2308.16212","DOI":"10.48550\/arXiv.2308.16212"},{"key":"ref121\/cit121","doi-asserted-by":"publisher","unstructured":"Saebi, M.; Nan, B.; Herr, J.; Wahlers, J.; Wiest, O.; Chawla, N. 2021, Graph Neural Networks for Predicting Chemical Reaction Performance ChemRxiv.10.26434\/chemrxiv-2021-2x06r-v3","DOI":"10.26434\/chemrxiv-2021-2x06r-v3"},{"key":"ref122\/cit122","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-021-00579-z"},{"key":"ref123\/cit123","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.27016"},{"key":"ref124\/cit124","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.2c01085"},{"key":"ref125\/cit125","doi-asserted-by":"publisher","DOI":"10.1021\/acsomega.2c05165"},{"key":"ref126\/cit126","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-39283-x"},{"key":"ref127\/cit127","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2212711119"},{"key":"ref128\/cit128","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01288-4"},{"key":"ref129\/cit129","doi-asserted-by":"publisher","DOI":"10.1002\/jcc.23790"},{"key":"ref130\/cit130","doi-asserted-by":"publisher","DOI":"10.1038\/s43588-022-00369-z"},{"key":"ref131\/cit131","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-03858-8"},{"key":"ref132\/cit132","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpca.8b10007"},{"key":"ref133\/cit133","doi-asserted-by":"publisher","DOI":"10.1021\/jp0455430"},{"key":"ref134\/cit134","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejmech.2008.09.052"},{"key":"ref135\/cit135","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.3c00102"},{"key":"ref136\/cit136","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c01480"},{"key":"ref137\/cit137","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpc.2016.02.013"},{"key":"ref138\/cit138","doi-asserted-by":"publisher","DOI":"10.1016\/j.fuel.2015.09.031"},{"key":"ref139\/cit139","doi-asserted-by":"publisher","DOI":"10.1002\/chem.201800345"},{"key":"ref140\/cit140","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.5b00373"},{"key":"ref141\/cit141","doi-asserted-by":"publisher","DOI":"10.1021\/jp052504v"},{"key":"ref142\/cit142","doi-asserted-by":"publisher","DOI":"10.1016\/S0009-2614(99)01399-8"},{"key":"ref143\/cit143","doi-asserted-by":"publisher","DOI":"10.1002\/cphc.202300114"},{"key":"ref144\/cit144","doi-asserted-by":"publisher","DOI":"10.1021\/jo0479213"},{"key":"ref145\/cit145","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemphys.2005.01.017"},{"key":"ref146\/cit146","doi-asserted-by":"publisher","DOI":"10.1021\/jp054449w"},{"key":"ref147\/cit147","doi-asserted-by":"publisher","DOI":"10.1021\/acs.accounts.0c00770"},{"key":"ref148\/cit148","doi-asserted-by":"publisher","DOI":"10.1021\/jacs.2c05302"},{"key":"ref149\/cit149","doi-asserted-by":"publisher","DOI":"10.1039\/D3SC03902A"},{"key":"ref150\/cit150","doi-asserted-by":"publisher","DOI":"10.1039\/D0SC04896H"},{"key":"ref151\/cit151","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpclett.0c00500"},{"key":"ref152\/cit152","doi-asserted-by":"publisher","DOI":"10.1038\/ncomms14621"},{"key":"ref153\/cit153","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.2c00816"},{"issue":"1","key":"ref154\/cit154","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1021\/acs.jctc.3c00710","volume":"20","author":"Gel\u017einyt\u0117 E.","year":"2024","journal-title":"J. Chem. Theory Comput."},{"key":"ref155\/cit155","doi-asserted-by":"publisher","DOI":"10.1038\/s43588-023-00563-7"},{"key":"ref156\/cit156","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-021-01453-z"},{"key":"ref157\/cit157","doi-asserted-by":"publisher","DOI":"10.1021\/ci200207y"},{"key":"ref158\/cit158","doi-asserted-by":"publisher","DOI":"10.1021\/ci3003039"},{"key":"ref159\/cit159","doi-asserted-by":"publisher","DOI":"10.1039\/D3CC03229A"},{"key":"ref160\/cit160","doi-asserted-by":"publisher","DOI":"10.1039\/C9CC05122H"},{"key":"ref161\/cit161","doi-asserted-by":"publisher","DOI":"10.1039\/D1SC02362D"},{"key":"ref162\/cit162","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.2c00588"},{"key":"ref163\/cit163","doi-asserted-by":"publisher","unstructured":"Sagawa, T.; Kojima, R. ReactionT5: A Large-Scale Pre-Trained Model Towards Application of Limited Reaction Data.  arXiv 2023.10.48550\/arXiv.2311.06708","DOI":"10.48550\/arXiv.2311.06708"},{"key":"ref164\/cit164","doi-asserted-by":"publisher","DOI":"10.1039\/D0QO01636E"},{"key":"ref165\/cit165","doi-asserted-by":"publisher","unstructured":"Schwaller, P.; Laino, T.; Gaudin, T.; Bolgar, P.; Bekas, C.;  Molecular Transformer - A Model for Uncertainty-Calibrated Chemical Reaction Prediction. 2018.10.48550\/arXiv.1811.02633","DOI":"10.48550\/arXiv.1811.02633"},{"key":"ref166\/cit166","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25640"},{"key":"ref167\/cit167","doi-asserted-by":"publisher","DOI":"10.1021\/acscentsci.7b00303"},{"key":"ref168\/cit168","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00801"},{"key":"ref169\/cit169","doi-asserted-by":"publisher","unstructured":"Chakraborty, A.; Thakkar, A.; Vaucher, A. C.; Laino, T. Data-Driven Reaction Template Fingerprints.  ChemRxiv, 2022.10.26434\/chemrxiv-2022-4kzp1","DOI":"10.26434\/chemrxiv-2022-4kzp1"},{"key":"ref170\/cit170","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3083838"},{"key":"ref171\/cit171","doi-asserted-by":"publisher","DOI":"10.1021\/acsomega.2c03812"},{"key":"ref172\/cit172","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00538"},{"key":"ref173\/cit173","doi-asserted-by":"publisher","DOI":"10.1038\/nature25978"},{"key":"ref174\/cit174","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c00699"},{"key":"ref175\/cit175","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330958"},{"key":"ref176\/cit176","doi-asserted-by":"publisher","unstructured":"Wang, H.; Li, W.; Jin, X.; Cho, K.; Ji, H.; Han, J.; Burke, M. D. Chemical-Reaction-Aware Molecule Representation Learning.  arXiv 202110.48550\/arXiv.2109.09888.","DOI":"10.48550\/arXiv.2109.09888"},{"key":"ref177\/cit177","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.2c00321"},{"key":"ref178\/cit178","doi-asserted-by":"publisher","DOI":"10.1039\/C8SC04228D"},{"key":"ref179\/cit179","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c00537"},{"key":"ref180\/cit180","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c00975"},{"key":"ref181\/cit181","doi-asserted-by":"publisher","DOI":"10.1109\/BIBM55620.2022.9995338"},{"key":"ref182\/cit182","doi-asserted-by":"publisher","DOI":"10.1039\/qr9480200107"},{"key":"ref183\/cit183","doi-asserted-by":"publisher","DOI":"10.1021\/ed049p400"},{"key":"ref184\/cit184","volume-title":"Aromaticity and Other Conjugation Effects","author":"Gleiter R.","year":"2012"},{"key":"ref185\/cit185","doi-asserted-by":"publisher","DOI":"10.1007\/s00285-007-0093-7"},{"key":"ref186\/cit186","doi-asserted-by":"publisher","DOI":"10.1002\/ejoc.201300731"},{"key":"ref187\/cit187","doi-asserted-by":"publisher","DOI":"10.1021\/ma501756p"},{"key":"ref188\/cit188","doi-asserted-by":"publisher","DOI":"10.1126\/science.abb6375"},{"key":"ref189\/cit189","doi-asserted-by":"publisher","DOI":"10.1016\/j.ces.2020.116089"},{"key":"ref190\/cit190","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-32191-6"},{"key":"ref191\/cit191","doi-asserted-by":"publisher","unstructured":"Oord, A. v. d.; Li, Y.; Vinyals, O. Representation Learning with Contrastive Predictive Coding.  arXiv 2018.10.48550\/arXiv.1807.03748","DOI":"10.48550\/arXiv.1807.03748"},{"key":"ref192\/cit192","doi-asserted-by":"publisher","unstructured":"Wu, Z.; Wang, S.; Gu, J.; Khabsa, M.; Sun, F.; Ma, H. CLEAR: Contrastive Learning for Sentence Representation.  arXiv 2020.10.48550\/arXiv.2012.15466","DOI":"10.48550\/arXiv.2012.15466"},{"key":"ref193\/cit193","doi-asserted-by":"publisher","unstructured":"Qin, Y.; Lin, Y.; Takanobu, R.; Liu, Z.; Li, P.; Ji, H.; Huang, M.; Sun, M.; Zhou, J. ERICA: Improving Entity and Relation Understanding for Pre-Trained Language Models Via Contrastive Learning.  arXiv 2020.10.48550\/arXiv.2012.15022","DOI":"10.48550\/arXiv.2012.15022"},{"key":"ref194\/cit194","doi-asserted-by":"publisher","unstructured":"Giorgi, J.; Nitski, O.; Wang, B.; Bader, G. DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations.  arXiv 2020.10.48550\/arXiv.2006.03659","DOI":"10.48550\/arXiv.2006.03659"},{"key":"ref195\/cit195","doi-asserted-by":"publisher","unstructured":"Miao, D.; Zhang, J.; Xie, W.; Song, J.; Li, X.; Jia, L.; Guo, N. Simple Contrastive Representation Adversarial Learning for NLP Tasks.  arXiv 2021.10.48550\/arXiv.2111.13301","DOI":"10.48550\/arXiv.2111.13301"},{"key":"ref196\/cit196","doi-asserted-by":"publisher","unstructured":"Tavakoli, M.; Shmakov, A.; Ceccarelli, F.; Baldi, P. Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation.  arXiv 2022.10.48550\/arXiv.2201.01196","DOI":"10.48550\/arXiv.2201.01196"},{"key":"ref197\/cit197","doi-asserted-by":"publisher","unstructured":"Liu, S.; Wang, H.; Liu, W.; Lasenby, J.; Guo, H.; Tang, J. Pre-Training Molecular Graph Representation with 3D Geometry.  arXiv. 2021.10.48550\/arXiv.2110.07728","DOI":"10.48550\/arXiv.2110.07728"},{"key":"ref198\/cit198","doi-asserted-by":"publisher","unstructured":"Liu, S.; Guo, H.; Tang, J. Molecular Geometry Pretraining with Se (3)-Invariant Denoising Distance Matching.  arXiv 2022.10.48550\/arXiv.2206.13602","DOI":"10.48550\/arXiv.2206.13602"},{"key":"ref199\/cit199","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539368"},{"key":"ref200\/cit200","doi-asserted-by":"publisher","unstructured":"Jiao, R.; Han, J.; Huang, W.; Rong, Y.; Liu, Y. Energy-Motivated Equivariant Pretraining for 3D Molecular Graphs.  arXiv 2022.10.48550\/arXiv.2207.08824","DOI":"10.48550\/arXiv.2207.08824"},{"key":"ref201\/cit201","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.25978"},{"key":"ref202\/cit202","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599252"},{"key":"ref203\/cit203","first-page":"20479","author":"St\u00e4rk H.","year":"2022","journal-title":"International Conference on Machine Learning"},{"key":"ref204\/cit204","doi-asserted-by":"publisher","unstructured":"Luo, S.; Chen, T.; Xu, Y.; Zheng, S.; Liu, T.Y.; Wang, L.; He, D. One Transformer Can Understand Both 2D & 3D Molecular Data.  arXiv 2022.10.48550\/arXiv.2210.01765","DOI":"10.48550\/arXiv.2210.01765"},{"key":"ref205\/cit205","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01870-w"},{"key":"ref206\/cit206","doi-asserted-by":"publisher","unstructured":"Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; Krueger, G.; Sutskever, I. Learning Transferable Visual Models From Natural Language Supervision.  arXiv 2021.10.48550\/arXiv.2103.00020","DOI":"10.48550\/arXiv.2103.00020"},{"key":"ref207\/cit207","doi-asserted-by":"publisher","unstructured":"Su, B.; Du, D.; Yang, Z.; Zhou, Y.; Li, J.; Rao, A.; Sun, H.; Lu, Z.; Wen, J.R. A Molecular Multimodal Foundation Model Associating Molecule Graphs with Natural Language.  arXiv 2022.10.48550\/arXiv.2209.05481","DOI":"10.48550\/arXiv.2209.05481"},{"key":"ref208\/cit208","first-page":"21497","author":"Liu S.","year":"2023","journal-title":"International Conference on Machine Learning"},{"key":"ref209\/cit209","doi-asserted-by":"publisher","DOI":"10.1101\/2023.11.02.565401"},{"key":"ref210\/cit210","doi-asserted-by":"publisher","unstructured":"Kaufman, B.; Williams, E.; Underkoffler, C.; Pederson, R.; Mardirossian, N.; Watson, I.; Parkhill, J. COATI: Multi-Modal Contrastive Pre-Training for Representing and Traversing Chemical Space.  ChemRxiv2023.10.26434\/chemrxiv-2023-bdkgm","DOI":"10.26434\/chemrxiv-2023-bdkgm"},{"key":"ref211\/cit211","doi-asserted-by":"publisher","unstructured":"Qian, Y.; Li, Z.; Tu, Z.; Coley, C. W.; Barzilay, R. Predictive Chemistry Augmented with Text Retrieval.  arXiv 2023.10.48550\/arXiv.2312.04881","DOI":"10.48550\/arXiv.2312.04881"}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.4c00004","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.4c00004","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,22]],"date-time":"2024-04-22T08:12:08Z","timestamp":1713773528000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.jcim.4c00004"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,15]]},"references-count":209,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024,4,22]]}},"alternative-id":["10.1021\/acs.jcim.4c00004"],"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.4c00004","relation":{},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,15]]}}}