{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,6]],"date-time":"2026-04-06T01:02:34Z","timestamp":1775437354728,"version":"3.50.1"},"reference-count":69,"publisher":"American Chemical Society (ACS)","issue":"11","license":[{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T00:00:00Z","timestamp":1684108800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"DOI":"10.13039\/501100008798","name":"Ministry of Science Research and Technology","doi-asserted-by":"publisher","award":["BSRF-math-399-06"],"award-info":[{"award-number":["BSRF-math-399-06"]}],"id":[{"id":"10.13039\/501100008798","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20A2068"],"award-info":[{"award-number":["U20A2068"]}],"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":[[2023,6,12]]},"DOI":"10.1021\/acs.jcim.3c00445","type":"journal-article","created":{"date-parts":[[2023,5,15]],"date-time":"2023-05-15T19:32:40Z","timestamp":1684179160000},"page":"3275-3287","source":"Crossref","is-referenced-by-count":12,"title":["FunQG: Molecular Representation Learning via Quotient Graphs"],"prefix":"10.1021","volume":"63","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8146-9764","authenticated-orcid":true,"given":"Hossein","family":"Hajiabolhassan","sequence":"first","affiliation":[{"name":"Department of Mathematics and Information Technology, Chair of Information Technology, Montanuniversit\u00e4t Leoben, Franz-Josef-Strasse 18, A-8700 Leoben, Austria"},{"name":"Machine Learning and Graph Mining Lab, Department of Applied Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, 19839-69411 Tehran, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3543-7857","authenticated-orcid":true,"given":"Zahra","family":"Taheri","sequence":"additional","affiliation":[{"name":"Machine Learning and Graph Mining Lab, Department of Applied Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, 19839-69411 Tehran, Iran"}]},{"given":"Ali","family":"Hojatnia","sequence":"additional","affiliation":[{"name":"Machine Learning and Graph Mining Lab, Department of Applied Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, 19839-69411 Tehran, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8703-7775","authenticated-orcid":true,"given":"Yavar Taheri","family":"Yeganeh","sequence":"additional","affiliation":[{"name":"Machine Learning and Graph Mining Lab, Department of Applied Mathematics, Faculty of Mathematical Sciences, Shahid Beheshti University, 19839-69411 Tehran, Iran"}]}],"member":"316","published-online":{"date-parts":[[2023,5,15]]},"reference":[{"key":"ref1\/cit1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tips.2019.06.004"},{"key":"ref2\/cit2","doi-asserted-by":"publisher","DOI":"10.1021\/acs.accounts.0c00699"},{"key":"ref3\/cit3","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1803294115"},{"key":"ref4\/cit4","doi-asserted-by":"publisher","DOI":"10.2174\/0929867327666200907141016"},{"key":"ref5\/cit5","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.9b00237"},{"key":"ref6\/cit6","first-page":"237","volume":"56","author":"Mauri A.","year":"2006","journal-title":"Match"},{"key":"ref7\/cit7","doi-asserted-by":"publisher","DOI":"10.1021\/ci100050t"},{"key":"ref8\/cit8","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btt105"},{"key":"ref9\/cit9","doi-asserted-by":"publisher","DOI":"10.1021\/ci010132r"},{"key":"ref10\/cit10","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-018-0258-y"},{"key":"ref11\/cit11","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.6b00601"},{"key":"ref12\/cit12","first-page":"2224","volume":"28","author":"Duvenaud D. K.","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref13\/cit13","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-016-9938-8"},{"key":"ref14\/cit14","first-page":"729","volume":"2","author":"Gori M.","year":"2005","journal-title":"Proceedings IEEE International Joint Conference on Neural Networks"},{"key":"ref15\/cit15","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2010350"},{"key":"ref16\/cit16","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"ref17\/cit17","unstructured":"Hamilton, W. L., Ying, R., Leskovec, J., Inductive representation learning on large graphs. In  Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017; pp.1025\u20131035."},{"key":"ref18\/cit18","unstructured":"Xu, K., Hu, W., Leskovec, J., Jegelka, S. How Powerful are Graph Neural Networks? In  International Conference on Learning Representations, 2018."},{"key":"ref19\/cit19","unstructured":"Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., Dahl, G. E. Neural message passing for quantum chemistry. In  International Conference on Machine Learning, 2017; pp 1263\u20131272."},{"key":"ref20\/cit20","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.9b00959"},{"key":"ref21\/cit21","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-020-00479-8"},{"key":"ref22\/cit22","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2020.01.021"},{"key":"ref23\/cit23","unstructured":"Kipf, T. N.; Welling, M. Semi-supervised classification with graph convolutional networks. In  Proceedings of the 5th International Conference on Learning Representations (ICLR\u201917), 2017, 24\u201326."},{"key":"ref24\/cit24","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y. Graph Attention Networks. In  Proceedings of 6th International Conference on Learning Representations, Vancouver, Canada, 2018."},{"key":"ref25\/cit25","doi-asserted-by":"crossref","unstructured":"Bai, J., Ren, Y., Zhang, J. Ripple walk training: A subgraph-based training framework for large and deep graph neural network. In  2021 International Joint Conference on Neural Networks (IJCNN), 2021; pp.1\u20138.","DOI":"10.1109\/IJCNN52387.2021.9533429"},{"key":"ref26\/cit26","unstructured":"Barcel\u00f3, P., Kostylev, E., Monet, M., P\u00e9rez, J., Reutter, J., Silva, J. P. The logical expressiveness of graph neural networks. In  8th International Conference on Learning Representations (ICLR 2020), 2020."},{"key":"ref27\/cit27","unstructured":"Alon, U., Yahav, E. On the Bottleneck of Graph Neural Networks and Its Practical Implications. In  International Conference on Learning Representations, 2020."},{"key":"ref28\/cit28","doi-asserted-by":"crossref","unstructured":"Li, Q., Han, Z., Wu, X. M. Deeper insights into graph convolutional networks for semi-supervised learning. In  Thirty-Second AAAI Conference on Artificial Intelligence, 2018.","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"ref29\/cit29","unstructured":"Oono, K., Suzuki, T. Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. In  International Conference on Learning Representations, 2019."},{"key":"ref30\/cit30","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref31\/cit31","first-page":"539","volume":"25","author":"Leskovec J.","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref32\/cit32","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"ref33\/cit33","volume-title":"Relational Representation Learning Workshop","author":"Shchur O.","year":"2018"},{"key":"ref34\/cit34","doi-asserted-by":"crossref","unstructured":"Matlock, M. K., Datta, A., Le Dang, N., Jiang, K., Swamidass, S. J. Deep learning long-range information in undirected graphs with wave networks. In  2019 International Joint Conference on Neural Networks (IJCNN), 2019; pp.1\u20138.","DOI":"10.1109\/IJCNN.2019.8852455"},{"key":"ref35\/cit35","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-021-00438-4"},{"key":"ref36\/cit36","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbab109"},{"key":"ref37\/cit37","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btac039"},{"key":"ref38\/cit38","first-page":"12559","volume":"33","author":"Rong Y.","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref39\/cit39","doi-asserted-by":"publisher","DOI":"10.1002\/jgt.3190130114"},{"key":"ref40\/cit40","doi-asserted-by":"publisher","DOI":"10.1137\/08074489X"},{"key":"ref41\/cit41","doi-asserted-by":"publisher","DOI":"10.1137\/080734029"},{"key":"ref42\/cit42","first-page":"7736","volume":"32","author":"Bravo Hermsdorff G.","year":"2019","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref43\/cit43","unstructured":"Cai, C., Wang, D., Wang, Y. Graph Coarsening with Neural Networks. In  International Conference on Learning Representations, 2020."},{"key":"ref44\/cit44","unstructured":"Loukas, A., Vandergheynst, P. Spectrally approximating large graphs with smaller graphs. In  International Conference on Machine Learning, 2018; pp 3237\u20133246."},{"key":"ref45\/cit45","first-page":"1","volume":"20","author":"Loukas A.","year":"2019","journal-title":"J. Mach. Learn. Res."},{"key":"ref46\/cit46","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-020-00460-5"},{"key":"ref47\/cit47","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-017-0225-z"},{"key":"ref48\/cit48","doi-asserted-by":"publisher","DOI":"10.1351\/pac199466051077"},{"key":"ref49\/cit49","doi-asserted-by":"publisher","DOI":"10.1039\/C7SC02664A"},{"key":"ref50\/cit50","unstructured":"Dai, H., Dai, B., Song, L. Discriminative embeddings of latent variable models for structured data. In  International Conference on Machine Learning, 2016; pp 2702\u20132711."},{"key":"ref51\/cit51","doi-asserted-by":"crossref","unstructured":"Mah\u0107, P., Ueda, N., Akutsu, T., Perret, J. L., Vert, J. P. Extensions of marginalized graph kernels. In  Proceedings of the Twenty-First International Conference on Machine Learning, 2004; p 70.","DOI":"10.1145\/1015330.1015446"},{"key":"ref52\/cit52","volume-title":"Graph Symmetry: Algebraic Methods and Applications","volume":"497","author":"Hahn G.","year":"2013"},{"key":"ref53\/cit53","volume-title":"Organic Chemistry: Concepts and Applications","author":"Headley A. D.","year":"2020"},{"key":"ref54\/cit54","doi-asserted-by":"publisher","DOI":"10.1016\/j.febslet.2005.07.039"},{"key":"ref55\/cit55","unstructured":"Hall, R. Implementation of an algorithm to identify functional groups in organic molecules, 2017. https:\/\/github.com\/rdkit\/rdkit\/tree\/master\/Contrib\/IFG (accessed 2022\u201304\u201316)."},{"key":"ref56\/cit56","unstructured":"Ramsundar, B., Kearnes, S., Riley, P., Webster, D., Konerding, D., Pande, V. Massively multitask networks for drug discovery.  arXiv Preprint, arXiv:1502.02072, 2015."},{"key":"ref57\/cit57","first-page":"992","author":"Sch\u00fctt K. T.","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref58\/cit58","doi-asserted-by":"crossref","unstructured":"Lu, C., Liu, Q., Wang, C., Huang, Z., Lin, P., He, L. Molecular property prediction: A multilevel quantum interactions modeling perspective. In  Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, 2019; pp 1052\u20131060.","DOI":"10.1609\/aaai.v33i01.33011052"},{"key":"ref59\/cit59","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbaa266"},{"key":"ref60\/cit60","first-page":"na","volume":"32","author":"Paszke A.","year":"2019","journal-title":"Advances in neural information processing systems"},{"key":"ref61\/cit61","unstructured":"Wang, M., Zheng, D., Ye, Z., Gan, Q., Li, M., Song, X., Zhou, J., Ma, C., Yu, L., Gai, Y., Xiao, T. Deep graph library: A graph-centric, highly-performant package for graph neural networks.  arXiv Preprint, arXiv:1909.01315, 2019."},{"key":"ref62\/cit62","unstructured":"Swanson, K. Message passing neural networks for molecular property prediction. Doctoral dissertation. Massachusetts Institute of Technology, 2019."},{"key":"ref63\/cit63","unstructured":"Liaw, R., Liang, E., Nishihara, R., Moritz, P., Gonzalez, J. E., Stoica, I. Tune: A research platform for distributed model selection and training.  arXiv Preprint, arXiv:1807.05118, 2018."},{"key":"ref64\/cit64","unstructured":"Landrum, G. RDKit: Open-source cheminformatics, 2006. https:\/\/www.rdkit.org (accessed 2022\u201304\u201316)."},{"key":"ref65\/cit65","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c01344"},{"key":"ref66\/cit66","doi-asserted-by":"publisher","DOI":"10.1039\/D1SC05180F"},{"key":"ref67\/cit67","unstructured":"Sundararajan, M., Taly, A.; Yan, Q. Axiomatic attribution for deep networks. In  International Conference on Machine Learning, 2017; pp 3319\u20133328."},{"key":"ref68\/cit68","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jpclett.1c03058"},{"key":"ref69\/cit69","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.2c00715"}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.3c00445","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.3c00445","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,12]],"date-time":"2023-06-12T08:14:15Z","timestamp":1686557655000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.jcim.3c00445"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,15]]},"references-count":69,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,6,12]]}},"alternative-id":["10.1021\/acs.jcim.3c00445"],"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.3c00445","relation":{},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,15]]}}}