{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T17:36:46Z","timestamp":1772127406440,"version":"3.50.1"},"reference-count":47,"publisher":"American Chemical Society (ACS)","issue":"11","license":[{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T00:00:00Z","timestamp":1635724800000},"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":["21403138"],"award-info":[{"award-number":["21403138"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["21473112"],"award-info":[{"award-number":["21473112"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["21673138"],"award-info":[{"award-number":["21673138"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Chem. Inf. Model."],"published-print":{"date-parts":[[2021,11,22]]},"DOI":"10.1021\/acs.jcim.1c01118","type":"journal-article","created":{"date-parts":[[2021,11,1]],"date-time":"2021-11-01T09:12:52Z","timestamp":1635757972000},"page":"5414-5424","source":"Crossref","is-referenced-by-count":7,"title":["A Comparative Study of Marginalized Graph Kernel and Message-Passing Neural Network"],"prefix":"10.1021","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4796-2912","authenticated-orcid":true,"given":"Yan","family":"Xiang","sequence":"first","affiliation":[{"name":"School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7424-5439","authenticated-orcid":true,"given":"Yu-Hang","family":"Tang","sequence":"additional","affiliation":[{"name":"Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0976-1987","authenticated-orcid":true,"given":"Guang","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Mathematics & School of Mechanical Engineering, Purdue University, West Lafayette, Indiana 47907, United States"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0783-8194","authenticated-orcid":true,"given":"Huai","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"316","published-online":{"date-parts":[[2021,11,1]]},"reference":[{"key":"ref1\/cit1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"ref2\/cit2","doi-asserted-by":"publisher","DOI":"10.1039\/C7SC02664A"},{"key":"ref3\/cit3","unstructured":"Sch\u00fctt, K. T.; Kindermans, P.J.; Sauceda, H. E.; Chmiela, S.; Tkatchenko, A.; M\u00fcller, K.R. SchNet: A Continuous-Filter Convolutional Neural Network for Modeling Quantum Interactions.\n                      ArXiv170608566 Phys. Stat.\n                      2017."},{"key":"ref4\/cit4","unstructured":"Gilmer, J.; Schoenholz, S. S.; Riley, P. F.; Vinyals, O.; Dahl, G. E. Neural Message Passing for Quantum Chemistry. In\n                      Proceedings of the 34th International Conference on Machine Learning\n                      ; PMLR, 2017; pp 1263\u20131272."},{"key":"ref5\/cit5","unstructured":"Klicpera, J.; Gross, J.; G\u00fcnnemann, S. Directional Message Passing for Molecular Graphs.\n                      ArXiv200303123 Phys. Stat.\n                      2020."},{"key":"ref6\/cit6","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c01224"},{"key":"ref7\/cit7","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.7b00577"},{"key":"ref8\/cit8","unstructured":"Anderson, B.; Hy, T.S.; Kondor, R. Cormorant: Covariant Molecular Neural Networks.\n                      ArXiv190604015 Phys. Stat.\n                      2019."},{"key":"ref9\/cit9","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011052"},{"key":"ref10\/cit10","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00410"},{"key":"ref11\/cit11","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-019-0407-y"},{"key":"ref12\/cit12","doi-asserted-by":"publisher","DOI":"10.1088\/2632-2153\/abf5b8"},{"key":"ref13\/cit13","unstructured":"J\u00f8rgensen, P. B.; Jacobsen, K. W.; Schmidt, M. N. Neural Message Passing with Edge Updates for Predicting Properties of Molecules and Materials.\n                      ArXiv180603146 Cs Stat.\n                      2018."},{"key":"ref14\/cit14","unstructured":"Hu, W.; Fey, M.; Ren, H.; Nakata, M.; Dong, Y.; Leskovec, J. Ogb-Lsc: A Large-Scale Challenge for Machine Learning on Graphs.\n                      ArXiv Prepr. ArXiv210309430\n                      2021."},{"key":"ref15\/cit15","unstructured":"Loukas, A. How Hard Is to Distinguish Graphs with Graph Neural Networks? In\n                      Advances in Neural Information Processing Systems 33 (NIPS 2020)\n                      ; 2020."},{"key":"ref16\/cit16","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00237"},{"key":"ref17\/cit17","doi-asserted-by":"publisher","DOI":"10.1007\/s41109-019-0195-3"},{"key":"ref18\/cit18","unstructured":"Kashima, H.; Tsuda, K.; Inokuchi, A. Marginalized Kernels Between Labeled Graphs. In\n                      Proceedings of the 20th International Conference on Machine Learning\n                      ; ICML \u201903; Washington DC, 2003; pp 321\u2013328."},{"key":"ref19\/cit19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1007\/978-3-540-45167-9_11","volume-title":"Learning Theory and Kernel Machines","author":"G\u00e4rtner T.","year":"2003"},{"key":"ref20\/cit20","doi-asserted-by":"publisher","DOI":"10.1145\/1015330.1015446"},{"key":"ref21\/cit21","doi-asserted-by":"publisher","DOI":"10.1145\/1102351.1102380"},{"key":"ref22\/cit22","unstructured":"Kriege, N.; Mutzel, P. Subgraph Matching Kernels for Attributed Graphs.\n                      ArXiv12066483 Cs Stat.\n                      2012."},{"key":"ref23\/cit23","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2016.0142"},{"key":"ref24\/cit24","unstructured":"Togninalli, M.; Ghisu, E.; Llinares-L\u00f3pez, F.; Rieck, B.; Borgwardt, K. Wasserstein Weisfeiler-Lehman Graph Kernels.\n                      ArXiv190601277 Cs Q-Bio Stat.\n                      2019."},{"key":"ref25\/cit25","doi-asserted-by":"crossref","unstructured":"Schulz, T. H.; Horv\u00e1th, T.; Welke, P.; Wrobel, S. A Generalized Weisfeiler-Lehman Graph Kernel.\n                      ArXiv210108104 Cs\n                      2021.","DOI":"10.1007\/s10994-022-06131-w"},{"key":"ref26\/cit26","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330918"},{"key":"ref27\/cit27","doi-asserted-by":"publisher","DOI":"10.11578\/dc.20191015.4"},{"key":"ref28\/cit28","doi-asserted-by":"publisher","DOI":"10.1109\/IPDPS47924.2020.00080"},{"key":"ref29\/cit29","doi-asserted-by":"publisher","DOI":"10.1063\/1.5078640"},{"key":"ref30\/cit30","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpca.1c02391"},{"key":"ref31\/cit31","doi-asserted-by":"publisher","DOI":"10.1039\/C8SC00148K"},{"key":"ref32\/cit32","unstructured":"Haussler, D.\n                      Convolution Kernels on Discrete Structures\n                      ; Technical report; Department of Computer Science, University of California at Santa Cruz: Santa Cruz, CA, 1999."},{"key":"ref33\/cit33","volume-title":"Gaussian Processes for Machine Learning","author":"Rasmussen C. E.","year":"2006"},{"key":"ref34\/cit34","first-page":"2825","volume":"12","author":"Pedregosa F.","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref35\/cit35","unstructured":"Nair, V.; Hinton, G. E. Rectified Linear Units Improve Restricted Boltzmann Machines;\n                      ICML '10: Proceedings of the 27th International Conference on International Conference on Machine Learning, June 2010\n                      ; pp 807\u2013814."},{"key":"ref36\/cit36","unstructured":"Descriptor computation\n(chemistry) and (optional) storage for machine\nlearning. https:\/\/github.com\/bp-kelley\/descriptastorus (accessed 2021-05-30)."},{"key":"ref37\/cit37","unstructured":"Distributed Asynchronous\nHyperparameter Optimization in Python. https:\/\/github.com\/hyperopt\/hyperopt (accessed 2021-05-30)."},{"key":"ref38\/cit38","unstructured":"Graph Kernel Machines\nfor Molecular Property Prediction. https:\/\/github.com\/Xiangyan93\/Chem-Graph-Kernel-Machine (accessed\n2021-05-30)."},{"key":"ref39\/cit39","unstructured":"Bergstra, J.; Bardenet, R.; Bengio, Y.; K\u00e9gl, B. Algorithms for Hyper-Parameter Optimization.\n                      Advances in Neural Information Processing Systems 24 (NIPS 2011)\n                      ; 2011; Vol. 24."},{"key":"ref40\/cit40","unstructured":"Bergstra, J.; Yamins, D.; Cox, D. Making a Science of Model Search: Hyperparameter Optimization in Hundreds of Dimensions for Vision Architectures. In\n                      International Conference on Machine Learning\n                      ; PMLR, 2013; pp 115\u2013123."},{"key":"ref41\/cit41","doi-asserted-by":"publisher","DOI":"10.1039\/D0SC06805E"},{"key":"ref42\/cit42","unstructured":"Dwivedi, V. P.; Joshi, C. K.; Laurent, T.; Bengio, Y.; Bresson, X. Benchmarking Graph Neural Networks.\n                      ArXiv200300982 Cs Stat.\n                      2020."},{"key":"ref43\/cit43","doi-asserted-by":"publisher","DOI":"10.1016\/0169-7439(87)80084-9"},{"key":"ref44\/cit44","doi-asserted-by":"publisher","DOI":"10.1137\/090771806"},{"key":"ref45\/cit45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1007\/BFb0020217","volume-title":"Artificial Neural Networks\u2500ICANN \u201997","author":"Sch\u00f6lkopf B.","year":"1997"},{"key":"ref46\/cit46","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00502"},{"key":"ref47\/cit47","unstructured":"Tang, Y.H.; Zhu, Y.; de Jong, W. A. Detecting Label Noise via Leave-One-Out Cross-Validation.\n                      ArXiv210311352 Cs Math Stat.\n                      2021."}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.1c01118","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,27]],"date-time":"2023-04-27T13:47:50Z","timestamp":1682603270000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.jcim.1c01118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,1]]},"references-count":47,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2021,11,22]]}},"alternative-id":["10.1021\/acs.jcim.1c01118"],"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.1c01118","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv.14706117.v1","asserted-by":"object"},{"id-type":"doi","id":"10.26434\/chemrxiv-2021-c3w1l-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":[[2021,11,1]]}}}