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In\n                      Proceedings of the 37th International Conference on Machine Learning\n                      , 2020; 4839\u20134848."},{"key":"ref5\/cit5","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-018-0286-7"},{"key":"ref6\/cit6","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-024-07487-w"},{"key":"ref7\/cit7","doi-asserted-by":"publisher","DOI":"10.3390\/molecules23102520"},{"key":"ref8\/cit8","doi-asserted-by":"publisher","DOI":"10.1021\/acs.accounts.0c00699"},{"key":"ref9\/cit9","doi-asserted-by":"publisher","DOI":"10.1016\/j.ddtec.2020.11.009"},{"key":"ref10\/cit10","doi-asserted-by":"publisher","DOI":"10.1039\/C7SC02664A"},{"key":"ref11\/cit11","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-17844-8"},{"key":"ref12\/cit12","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-020-00460-5"},{"key":"ref13\/cit13","doi-asserted-by":"crossref","unstructured":"Grisoni, F.; Ballabio, D.; Todeschini, R.; Consonni, V. 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In\n                      International Conference on Learning Representations\n                      , 2019."},{"key":"ref20\/cit20","unstructured":"Kipf, T. N.; Welling, M. Semi-Supervised Classification with Graph Convolutional Networks. In\n                      International Conference on Learning Representations\n                      , 2017."},{"key":"ref21\/cit21","unstructured":"Veli\u010dkovi\u0107, P.; Cucurull, G.; Casanova, A.; Romero, A.; Li\u00f2, P.; Bengio, Y. Graph Attention Networks. In\n                      International Conference on Learning Representations\n                      , 2018."},{"key":"ref22\/cit22","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"ref23\/cit23","doi-asserted-by":"crossref","unstructured":"Li, Z.; Shen, X.; Jiao, Y.; Pan, X.; Zou, P.; Meng, X.; Yao, C.; Bu, J. Hierarchical Bipartite Graph Neural Networks: Towards Large-Scale E-commerce Applications. 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In\n                      Advances in Neural Information Processing Systems\n                      , 2018."},{"key":"ref26\/cit26","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-020-00479-8"},{"key":"ref27\/cit27","doi-asserted-by":"publisher","DOI":"10.1038\/s43246-022-00315-6"},{"key":"ref28\/cit28","unstructured":"Rong, Y.; Bian, Y.; Xu, T.; Xie, W.; Wei, Y.; Huang, W.; Huang, J.Self-Supervised Graph Transformer on Large-Scale Molecular Data. In\n                      Advances in Neural Information Processing Systems\n                      , Newry, UK, 2020; pp 12559\u201312571."},{"key":"ref29\/cit29","doi-asserted-by":"crossref","unstructured":"Hu, Z.; Dong, Y.; Wang, K.; Chang, K.W.; Sun, Y. GPT-GNN: Generative Pre-Training of Graph Neural Networks. In\n                      Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining\n                      , New York, NY, USA, 2020; pp 1857\u20131867.","DOI":"10.1145\/3394486.3403237"},{"key":"ref30\/cit30","unstructured":"Rong, Y.; Huang, W.; Xu, T.; Huang, J. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In\n                      International Conference on Learning Representations\n                      , 2020."},{"key":"ref31\/cit31","doi-asserted-by":"crossref","unstructured":"Zhang, M.; Hu, L.; Shi, C.; Wang, X.Adversarial Label-Flipping Attack and Defense for Graph Neural Networks. 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In\n                      Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining\n                      , 2021; pp 3585\u20133594.","DOI":"10.1145\/3447548.3467186"},{"key":"ref36\/cit36","unstructured":"Zhang, Z.; Liu, Q.; Wang, H.; Lu, C.; Lee, C.K. Motif-based graph self-supervised learning for molecular property prediction.\n                      Advances in Neural Information Processing Systems\n                      , 2021, Vol. 34, pp 15870\u201315882."},{"key":"ref37\/cit37","unstructured":"Wang, H.; Li, W.; Jin, X.; Cho, K.; Ji, H.; Han, J.; Burke, M. D. Chemical-Reaction-Aware Molecule Representation Learning. In\n                      International Conference on Learning Representations\n                      , 2022."},{"key":"ref38\/cit38","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2024.3364059"},{"key":"ref39\/cit39","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.2c00495"},{"key":"ref40\/cit40","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-022-00447-x"},{"key":"ref41\/cit41","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-43214-1"},{"key":"ref42\/cit42","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-19-6631-6_26"},{"key":"ref43\/cit43","doi-asserted-by":"publisher","DOI":"10.1016\/j.chempr.2020.05.002"},{"key":"ref44\/cit44","unstructured":"Jiang, M.; Liu, K.; Zhong, M.; Schaeffer, R.; Ouyang, S.; Han, J.; Koyejo, S. Does Data Contamination Make a Difference? Insights from Intentionally Contaminating Pre-training Data For Language Models. In\n                      ICLR 2024 Workshop on Navigating and Addressing Data Problems for Foundation Models\n                      , 2024."},{"key":"ref45\/cit45","doi-asserted-by":"publisher","DOI":"10.1021\/ci0200570"},{"key":"ref46\/cit46","unstructured":"Hamilton, W.; Ying, Z.; Leskovec, J. Inductive Representation Learning on Large Graphs. In\n                      Advances in Neural Information Processing Systems\n                      , 2017."},{"key":"ref47\/cit47","doi-asserted-by":"crossref","unstructured":"Hu, Z.; Dong, Y.; Wang, K.; Chang, K.W.; Sun, Y. GPT-GNN: Generative pre-training of graph neural networks. In\n                      Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining\n                      , 2020; pp 1857\u20131867.","DOI":"10.1145\/3394486.3403237"},{"key":"ref48\/cit48","unstructured":"Hu, W.; Liu, B.; Gomes, J.; Zitnik, M.; Liang, P.; Pande, V.; Leskovec, J. Strategies for pre-training graph neural networks.\n                      arXiv Preprint\n                      , arXiv:1905.12265, 2019."},{"key":"ref49\/cit49","unstructured":"Sun, F.Y.; Hoffmann, J.; Verma, V.; Tang, J. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization.\n                      arXiv Preprint\n                      , arXiv:1908.01000, 2019."},{"key":"ref50\/cit50","unstructured":"Xu, M.; Wang, H.; Ni, B.; Guo, H.; Tang, J. Self-supervised graph-level representation learning with local and global structure. In\n                      International Conference on Machine Learning\n                      , 2021; pp 11548\u201311558."},{"key":"ref51\/cit51","unstructured":"You, Y.; Chen, T.; Sui, Y.; Chen, T.; Wang, Z.; Shen, Y.Graph contrastive learning with augmentations.\n                      Advances in Neural Information Processing Systems\n                      , 2020; Vol. 33, pp 5812\u20135823."},{"key":"ref52\/cit52","unstructured":"You, Y.; Chen, T.; Shen, Y.; Wang, Z. Graph contrastive learning automated. In\n                      International Conference on Machine Learning\n                      , 2021; pp 12121\u201312132."},{"key":"ref53\/cit53","doi-asserted-by":"publisher","DOI":"10.1039\/C7SC02664A"},{"key":"ref54\/cit54","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-38040-3_52"},{"key":"ref55\/cit55","doi-asserted-by":"crossref","unstructured":"DenAdel, A.; Hughes, M.; Thoutam, A.; Gupta, A.; Navia, A. W.; Fusi, N.; Raghavan, S.; Winter, P. S.; Amini, A. P.; Crawford, L. Evaluating the role of pre-training dataset size and diversity on single-cell foundation model performance.\n                      bioRxiv Preprint\n                      , 2024.","DOI":"10.1101\/2024.12.13.628448"},{"key":"ref56\/cit56","doi-asserted-by":"crossref","unstructured":"Davis, J.; Goadrich, M. The relationship between Precision-Recall and ROC curves. In\n                      Proceedings of the 23rd International Conference on Machine Learning\n                      , 2006; pp 233\u2013240.","DOI":"10.1145\/1143844.1143874"},{"key":"ref57\/cit57","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0118432"},{"key":"ref58\/cit58","doi-asserted-by":"publisher","DOI":"10.1186\/s13040-023-00322-4"},{"key":"ref59\/cit59","doi-asserted-by":"publisher","DOI":"10.1186\/s13040-021-00244-z"},{"key":"ref60\/cit60","doi-asserted-by":"publisher","DOI":"10.1186\/s12864-019-6413-7"},{"key":"ref61\/cit61","doi-asserted-by":"publisher","DOI":"10.1016\/j.cpc.2019.106949"},{"key":"ref62\/cit62","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.1c00975"},{"key":"ref63\/cit63","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.0c00866"},{"key":"ref64\/cit64","unstructured":"Landrum, G.\n                      RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling\n                      , 20135281."},{"key":"ref65\/cit65","doi-asserted-by":"publisher","DOI":"10.1002\/cmdc.200800178"},{"key":"ref66\/cit66","doi-asserted-by":"publisher","DOI":"10.1021\/ed040p295"},{"key":"ref67\/cit67","unstructured":"Ying, C.; Cai, T.; Luo, S.; Zheng, S.; Ke, G.; He, D.; Shen, Y.; Liu, T.Y. Do transformers really perform badly for graph representation? In\n                      Advances in neural information processing systems\n                      , 2021; Vol. 34, pp 28877\u201328888."},{"key":"ref68\/cit68","volume-title":"Mathematical statistics: basic ideas and selected topics, volumes I-II package","author":"Bickel P. J.","year":"2015"},{"key":"ref69\/cit69","doi-asserted-by":"crossref","unstructured":"Girshick, R. Fast R-CNN. In\n                      2015 IEEE International Conference on Computer Vision (ICCV)\n                      , 2015; pp 1440\u20131448.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref70\/cit70","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.5b00559"},{"key":"ref71\/cit71","doi-asserted-by":"publisher","DOI":"10.1016\/j.ymeth.2014.08.005"},{"key":"ref72\/cit72","doi-asserted-by":"publisher","DOI":"10.1021\/ci100050t"},{"key":"ref73\/cit73","first-page":"2825","volume":"12","author":"Pedregosa F.","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"ref74\/cit74","first-page":"2579","volume":"9","author":"van der Maaten L.","year":"2008","journal-title":"Journal of Machine Learning Research"}],"container-title":["Journal of Chemical Information and Modeling"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.5c01068","content-type":"application\/pdf","content-version":"vor","intended-application":"unspecified"},{"URL":"https:\/\/pubs.acs.org\/doi\/pdf\/10.1021\/acs.jcim.5c01068","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,22]],"date-time":"2025-09-22T08:10:33Z","timestamp":1758528633000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubs.acs.org\/doi\/10.1021\/acs.jcim.5c01068"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,8]]},"references-count":74,"journal-issue":{"issue":"18","published-print":{"date-parts":[[2025,9,22]]}},"alternative-id":["10.1021\/acs.jcim.5c01068"],"URL":"https:\/\/doi.org\/10.1021\/acs.jcim.5c01068","relation":{"has-preprint":[{"id-type":"doi","id":"10.26434\/chemrxiv-2025-0c3rz","asserted-by":"object"}]},"ISSN":["1549-9596","1549-960X"],"issn-type":[{"value":"1549-9596","type":"print"},{"value":"1549-960X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,8]]}}}