{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T20:16:49Z","timestamp":1762460209709,"version":"build-2065373602"},"publisher-location":"New York, NY, USA","reference-count":70,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDB38030300"],"award-info":[{"award-number":["XDB38030300"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,21]]},"DOI":"10.1145\/3627673.3679628","type":"proceedings-article","created":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T19:34:21Z","timestamp":1729452861000},"page":"1586-1596","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["MOAT: Graph Prompting for 3D Molecular Graphs"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-7265-3101","authenticated-orcid":false,"given":"Qingqing","family":"Long","sequence":"first","affiliation":[{"name":"CNIC, CAS & UCAS, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-8672-4468","authenticated-orcid":false,"given":"Yuchen","family":"Yan","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1404-3971","authenticated-orcid":false,"given":"Wentao","family":"Cui","sequence":"additional","affiliation":[{"name":"CNIC, CAS & UCAS, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9657-951X","authenticated-orcid":false,"given":"Wei","family":"Ju","sequence":"additional","affiliation":[{"name":"Sichuan University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4530-5516","authenticated-orcid":false,"given":"Zhihong","family":"Zhu","sequence":"additional","affiliation":[{"name":"Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2144-1131","authenticated-orcid":false,"given":"Yuanchun","family":"Zhou","sequence":"additional","affiliation":[{"name":"CNIC, CAS & UCAS, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5222-248X","authenticated-orcid":false,"given":"Xuezhi","family":"Wang","sequence":"additional","affiliation":[{"name":"CNIC, CAS & UCAS, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5294-5776","authenticated-orcid":false,"given":"Meng","family":"Xiao","sequence":"additional","affiliation":[{"name":"CNIC, CAS & UCAS, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Truong Son Hy, and Risi Kondor","author":"Anderson Brandon","year":"2019","unstructured":"Brandon Anderson, Truong Son Hy, and Risi Kondor. 2019. Cormorant: Covariant molecular neural networks. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_2_1","volume-title":"Nature communications","author":"Batzner Simon","year":"2022","unstructured":"Simon Batzner, Albert Musaelian, Lixin Sun, Mario Geiger, Jonathan P Mailoa, Mordechai Kornbluth, Nicola Molinari, Tess E Smidt, and Boris Kozinsky. 2022. E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials. Nature communications, Vol. 13, 1 (2022), 2453."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-020-00456-1"},{"key":"e_1_3_2_1_4_1","volume-title":"Chemistry: The central science. UpperSaddle River.","author":"Brown Theodore L","year":"2006","unstructured":"Theodore L Brown, HE LeMay, and Bruce E Bursten. 2006. Chemistry: The central science. UpperSaddle River."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42004-023-01045-7"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1021\/acscatal.0c04525"},{"key":"e_1_3_2_1_7_1","volume-title":"Molcpt: Molecule continuous prompt tuning to generalize molecular representation learning. arXiv preprint arXiv:2212.10614","author":"Diao Cameron","year":"2022","unstructured":"Cameron Diao, Kaixiong Zhou, Zirui Liu, Xiao Huang, and Xia Hu. 2022. Molcpt: Molecule continuous prompt tuning to generalize molecular representation learning. arXiv preprint arXiv:2212.10614 (2022)."},{"key":"e_1_3_2_1_8_1","volume-title":"Unified language model pre-training for natural language understanding and generation. Advances in neural information processing systems","author":"Dong Li","year":"2019","unstructured":"Li Dong, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng Gao, Ming Zhou, and Hsiao-Wuen Hon. 2019. Unified language model pre-training for natural language understanding and generation. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jmedchem.0c00754"},{"key":"e_1_3_2_1_10_1","volume-title":"Universal Prompt Tuning for Graph Neural Networks. arXiv preprint arXiv:2209.15240","author":"Fang Taoran","year":"2022","unstructured":"Taoran Fang, Yunchao Zhang, Yang Yang, Chunping Wang, and Lei Chen. 2022. Universal Prompt Tuning for Graph Neural Networks. arXiv preprint arXiv:2209.15240 (2022)."},{"key":"e_1_3_2_1_11_1","volume-title":"Knowledge graph-enhanced molecular contrastive learning with functional prompt. Nature Machine Intelligence","author":"Fang Yin","year":"2023","unstructured":"Yin Fang, Qiang Zhang, Ningyu Zhang, Zhuo Chen, Xiang Zhuang, Xin Shao, Xiaohui Fan, and Huajun Chen. 2023. Knowledge graph-enhanced molecular contrastive learning with functional prompt. Nature Machine Intelligence (2023), 1--12."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467430"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512187"},{"key":"e_1_3_2_1_14_1","volume-title":"3d roto-translation equivariant attention networks. Advances in neural information processing systems","author":"Fuchs Fabian","year":"2020","unstructured":"Fabian Fuchs, Daniel Worrall, Volker Fischer, and Max Welling. 2020. Se (3)-transformers: 3d roto-translation equivariant attention networks. Advances in neural information processing systems, Vol. 33 (2020), 1970--1981."},{"key":"e_1_3_2_1_15_1","first-page":"6790","article-title":"Gemnet: Universal directional graph neural networks for molecules","volume":"34","author":"Gasteiger Johannes","year":"2021","unstructured":"Johannes Gasteiger, Florian Becker, and Stephan G\u00fcnnemann. 2021. Gemnet: Universal directional graph neural networks for molecules. Advances in Neural Information Processing Systems, Vol. 34 (2021), 6790--6802.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_16_1","volume-title":"Fast and uncertainty-aware directional message passing for non-equilibrium molecules. arXiv preprint arXiv:2011.14115","author":"Gasteiger Johannes","year":"2020","unstructured":"Johannes Gasteiger, Shankari Giri, Johannes T Margraf, and Stephan G\u00fcnnemann. 2020. Fast and uncertainty-aware directional message passing for non-equilibrium molecules. arXiv preprint arXiv:2011.14115 (2020)."},{"key":"e_1_3_2_1_17_1","volume-title":"Directional message passing for molecular graphs. arXiv preprint arXiv:2003.03123","author":"Gasteiger Johannes","year":"2020","unstructured":"Johannes Gasteiger, Janek Gro\u00df, and Stephan G\u00fcnnemann. 2020. Directional message passing for molecular graphs. arXiv preprint arXiv:2003.03123 (2020)."},{"key":"e_1_3_2_1_18_1","volume-title":"Klaus-Robert M\u00fcller, and Kristof T Sch\u00fctt.","author":"Gebauer Niklas WA","year":"2022","unstructured":"Niklas WA Gebauer, Michael Gastegger, Stefaan SP Hessmann, Klaus-Robert M\u00fcller, and Kristof T Sch\u00fctt. 2022. Inverse design of 3d molecular structures with conditional generative neural networks. Nature communications, Vol. 13, 1 (2022), 973."},{"key":"e_1_3_2_1_19_1","volume-title":"Unifying principles of bifunctional, proximity-inducing small molecules. Nature chemical biology","author":"Gerry Christopher J","year":"2020","unstructured":"Christopher J Gerry and Stuart L Schreiber. 2020. Unifying principles of bifunctional, proximity-inducing small molecules. Nature chemical biology, Vol. 16, 4 (2020), 369--378."},{"key":"e_1_3_2_1_20_1","volume-title":"Simple gnn regularisation for 3d molecular property prediction & beyond. arXiv preprint arXiv:2106.07971","author":"Godwin Jonathan","year":"2021","unstructured":"Jonathan Godwin, Michael Schaarschmidt, Alexander Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velivckovi\u0107, James Kirkpatrick, and Peter Battaglia. 2021. Simple gnn regularisation for 3d molecular property prediction & beyond. arXiv preprint arXiv:2106.07971 (2021)."},{"key":"e_1_3_2_1_21_1","volume-title":"Forcenet: A graph neural network for large-scale quantum calculations. arXiv preprint arXiv:2103.01436","author":"Hu Weihua","year":"2021","unstructured":"Weihua Hu, Muhammed Shuaibi, Abhishek Das, Siddharth Goyal, Anuroop Sriram, Jure Leskovec, Devi Parikh, and C Lawrence Zitnick. 2021. Forcenet: A graph neural network for large-scale quantum calculations. arXiv preprint arXiv:2103.01436 (2021)."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403237"},{"key":"e_1_3_2_1_23_1","volume-title":"Pascal Frossard, Max Welling, Michael Bronstein, and Bruno Correia.","author":"Igashov Ilia","year":"2024","unstructured":"Ilia Igashov, Hannes St\u00e4rk, Cl\u00e9ment Vignac, Arne Schneuing, Victor Garcia Satorras, Pascal Frossard, Max Welling, Michael Bronstein, and Bruno Correia. 2024. Equivariant 3D-conditional diffusion model for molecular linker design. Nature Machine Intelligence (2024), 1--11."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-19827-4_41"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i7.25978"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Wei Ju Zheng Fang Yiyang Gu Zequn Liu Qingqing Long Ziyue Qiao Yifang Qin Jianhao Shen Fang Sun Zhiping Xiao et al. 2024. A comprehensive survey on deep graph representation learning. Neural Networks (2024) 106207.","DOI":"10.1016\/j.neunet.2024.106207"},{"key":"e_1_3_2_1_27_1","unstructured":"Wei Ju Yifan Wang Yifang Qin Zhengyang Mao Zhiping Xiao Junyu Luo Junwei Yang Yiyang Gu Dongjie Wang Qingqing Long et al. 2024. Towards Graph Contrastive Learning: A Survey and Beyond. arXiv preprint arXiv:2405.11868 (2024)."},{"key":"e_1_3_2_1_28_1","volume-title":"A survey of data-efficient graph learning. arXiv preprint arXiv:2402.00447","author":"Ju Wei","year":"2024","unstructured":"Wei Ju, Siyu Yi, Yifan Wang, Qingqing Long, Junyu Luo, Zhiping Xiao, and Ming Zhang. 2024. A survey of data-efficient graph learning. arXiv preprint arXiv:2402.00447 (2024)."},{"key":"e_1_3_2_1_29_1","volume-title":"Compressed graph representation for scalable molecular graph generation. Journal of Cheminformatics","author":"Kwon Youngchun","year":"2020","unstructured":"Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, Kyoham Shin, and Seokho Kang. 2020. Compressed graph representation for scalable molecular graph generation. Journal of Cheminformatics (2020)."},{"key":"e_1_3_2_1_30_1","first-page":"31","article-title":"RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling","volume":"8","author":"Greg Landrum","year":"2013","unstructured":"Greg Landrum et al. 2013. RDKit: A software suite for cheminformatics, computational chemistry, and predictive modeling. Greg Landrum, Vol. 8 (2013), 31.","journal-title":"Greg Landrum"},{"key":"e_1_3_2_1_31_1","volume-title":"Equiformer: Equivariant graph attention transformer for 3d atomistic graphs. arXiv preprint arXiv:2206.11990","author":"Liao Yi-Lun","year":"2023","unstructured":"Yi-Lun Liao and Tess Smidt. 2023. Equiformer: Equivariant graph attention transformer for 3d atomistic graphs. arXiv preprint arXiv:2206.11990 (2023)."},{"key":"e_1_3_2_1_32_1","first-page":"1","article-title":"Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing","volume":"55","author":"Liu Pengfei","year":"2023","unstructured":"Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. Comput. Surveys, Vol. 55, 9 (2023), 1--35.","journal-title":"Comput. Surveys"},{"key":"e_1_3_2_1_33_1","volume-title":"Pre-training molecular graph representation with 3d geometry. arXiv preprint arXiv:2110.07728","author":"Liu Shengchao","year":"2021","unstructured":"Shengchao Liu, Hanchen Wang, Weiyang Liu, Joan Lasenby, Hongyu Guo, and Jian Tang. 2021. Pre-training molecular graph representation with 3d geometry. arXiv preprint arXiv:2110.07728 (2021)."},{"key":"e_1_3_2_1_34_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Liu Yi","year":"2022","unstructured":"Yi Liu, Limei Wang, Meng Liu, Yuchao Lin, Xuan Zhang, Bora Oztekin, and Shuiwang Ji. 2022. Spherical message passing for 3d molecular graphs. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583386"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645688"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403150"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449951"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357947"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467429"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33011052"},{"key":"e_1_3_2_1_42_1","volume-title":"Advances in Neural Information Processing Systems","volume":"36","author":"Luong Kha-Dinh","year":"2024","unstructured":"Kha-Dinh Luong and Ambuj K Singh. 2024. Fragment-based Pretraining and Finetuning on Molecular Graphs. Advances in Neural Information Processing Systems, Vol. 36 (2024)."},{"key":"e_1_3_2_1_43_1","volume-title":"Provably powerful graph networks. Advances in neural information processing systems","author":"Maron Haggai","year":"2019","unstructured":"Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman. 2019. Provably powerful graph networks. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_44_1","unstructured":"John E McMurry. 2014. Organic chemistry with biological applications. Cengage Learning."},{"key":"e_1_3_2_1_45_1","volume-title":"Quantum chemistry structures and properties of 134 kilo molecules. Scientific data","author":"Ramakrishnan Raghunathan","year":"2014","unstructured":"Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld. 2014. Quantum chemistry structures and properties of 134 kilo molecules. Scientific data, Vol. 1, 1 (2014), 1--7."},{"key":"e_1_3_2_1_46_1","first-page":"12559","article-title":"Self-supervised graph transformer on large-scale molecular data","volume":"33","author":"Rong Yu","year":"2020","unstructured":"Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang. 2020. Self-supervised graph transformer on large-scale molecular data. Advances in Neural Information Processing Systems, Vol. 33 (2020), 12559--12571.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci0100548"},{"key":"e_1_3_2_1_48_1","volume-title":"Mt-cgcnn: Integrating crystal graph convolutional neural network with multitask learning for material property prediction. arXiv preprint arXiv:1811.05660","author":"Sanyal Soumya","year":"2018","unstructured":"Soumya Sanyal, Janakiraman Balachandran, Naganand Yadati, Abhishek Kumar, Padmini Rajagopalan, Suchismita Sanyal, and Partha Talukdar. 2018. Mt-cgcnn: Integrating crystal graph convolutional neural network with multitask learning for material property prediction. arXiv preprint arXiv:1811.05660 (2018)."},{"key":"e_1_3_2_1_49_1","volume-title":"Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert M\u00fcller.","author":"Sch\u00fctt Kristof","year":"2017","unstructured":"Kristof Sch\u00fctt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert M\u00fcller. 2017. Schnet: A continuous-filter convolutional neural network for modeling quantum interactions. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_50_1","volume-title":"International Conference on Machine Learning. PMLR, 9377--9388","author":"Sch\u00fctt Kristof","year":"2021","unstructured":"Kristof Sch\u00fctt, Oliver Unke, and Michael Gastegger. 2021. Equivariant message passing for the prediction of tensorial properties and molecular spectra. In International Conference on Machine Learning. PMLR, 9377--9388."},{"key":"e_1_3_2_1_51_1","volume-title":"Towards training billion parameter graph neural networks for atomic simulations. arXiv preprint arXiv:2203.09697","author":"Sriram Anuroop","year":"2022","unstructured":"Anuroop Sriram, Abhishek Das, Brandon M Wood, Siddharth Goyal, and C Lawrence Zitnick. 2022. Towards training billion parameter graph neural networks for atomic simulations. arXiv preprint arXiv:2203.09697 (2022)."},{"key":"e_1_3_2_1_52_1","volume-title":"International Conference on Machine Learning. PMLR","author":"St\u00e4rk Hannes","year":"2022","unstructured":"Hannes St\u00e4rk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan G\u00fcnnemann, and Pietro Li\u00f2. 2022. 3d infomax improves gnns for molecular property prediction. In International Conference on Machine Learning. PMLR, 20479--20502."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539249"},{"key":"e_1_3_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599256"},{"key":"e_1_3_2_1_55_1","first-page":"15920","article-title":"Adversarial graph augmentation to improve graph contrastive learning","volume":"34","author":"Suresh Susheel","year":"2021","unstructured":"Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville. 2021. Adversarial graph augmentation to improve graph contrastive learning. Advances in Neural Information Processing Systems, Vol. 34 (2021), 15920--15933.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_56_1","volume-title":"Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds. arXiv preprint arXiv:1802.08219","author":"Thomas Nathaniel","year":"2018","unstructured":"Nathaniel Thomas, Tess Smidt, Steven Kearnes, Lusann Yang, Li Li, Kai Kohlhoff, and Patrick Riley. 2018. Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds. arXiv preprint arXiv:1802.08219 (2018)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.9b00181"},{"key":"e_1_3_2_1_58_1","first-page":"650","article-title":"ComENet: Towards complete and efficient message passing for 3D molecular graphs","volume":"35","author":"Wang Limei","year":"2022","unstructured":"Limei Wang, Yi Liu, Yuchao Lin, Haoran Liu, and Shuiwang Ji. 2022. ComENet: Towards complete and efficient message passing for 3D molecular graphs. Advances in Neural Information Processing Systems, Vol. 35 (2022), 650--664.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599252"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41570-020-0200-5"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512156"},{"key":"e_1_3_2_1_62_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Xia Jun","year":"2022","unstructured":"Jun Xia, Chengshuai Zhao, Bozhen Hu, Zhangyang Gao, Cheng Tan, Yue Liu, Siyuan Li, and Stan Z Li. 2022. Mole-bert: Rethinking pre-training graph neural networks for molecules. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_63_1","volume-title":"International Conference on Machine Learning. PMLR, 11548--11558","author":"Xu Minghao","year":"2021","unstructured":"Minghao Xu, Hang Wang, Bingbing Ni, Hongyu Guo, and Jian Tang. 2021. Self-supervised graph-level representation learning with local and global structure. In International Conference on Machine Learning. PMLR, 11548--11558."},{"key":"e_1_3_2_1_64_1","volume-title":"Molecule3d: A benchmark for predicting 3d geometries from molecular graphs. arXiv preprint arXiv:2110.01717","author":"Xu Zhao","year":"2021","unstructured":"Zhao Xu, Youzhi Luo, Xuan Zhang, Xinyi Xu, Yaochen Xie, Meng Liu, Kaleb Dickerson, Cheng Deng, Maho Nakata, and Shuiwang Ji. 2021. Molecule3d: A benchmark for predicting 3d geometries from molecular graphs. arXiv preprint arXiv:2110.01717 (2021)."},{"key":"e_1_3_2_1_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645620"},{"key":"e_1_3_2_1_66_1","volume-title":"International Conference on Machine Learning. PMLR, 12121--12132","author":"You Yuning","year":"2021","unstructured":"Yuning You, Tianlong Chen, Yang Shen, and Zhangyang Wang. 2021. Graph contrastive learning automated. In International Conference on Machine Learning. PMLR, 12121--12132."},{"key":"e_1_3_2_1_67_1","volume-title":"Graph contrastive learning with augmentations. Advances in neural information processing systems","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. Advances in neural information processing systems, Vol. 33 (2020), 5812--5823."},{"key":"e_1_3_2_1_68_1","volume-title":"HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning. ArXiv","author":"Yu Xingtong","year":"2023","unstructured":"Xingtong Yu, Yuan Fang, Zemin Liu, and Xinming Zhang. 2023. HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning. ArXiv, Vol. abs\/2312.01878 (2023)."},{"key":"e_1_3_2_1_69_1","volume-title":"Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, and Jonathan Godwin.","author":"Zaidi Sheheryar","year":"2022","unstructured":"Sheheryar Zaidi, Michael Schaarschmidt, James Martens, Hyunjik Kim, Yee Whye Teh, Alvaro Sanchez-Gonzalez, Peter Battaglia, Razvan Pascanu, and Jonathan Godwin. 2022. Pre-training via denoising for molecular property prediction. arXiv preprint arXiv:2206.00133 (2022)."},{"key":"e_1_3_2_1_70_1","first-page":"15870","article-title":"Motif-based graph self-supervised learning for molecular property prediction","volume":"34","author":"Zhang Zaixi","year":"2021","unstructured":"Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee. 2021. Motif-based graph self-supervised learning for molecular property prediction. Advances in Neural Information Processing Systems, Vol. 34 (2021), 15870--15882.","journal-title":"Advances in Neural Information Processing Systems"}],"event":{"name":"CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Boise ID USA","acronym":"CIKM '24"},"container-title":["Proceedings of the 33rd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679628","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627673.3679628","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:12Z","timestamp":1750294692000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679628"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":70,"alternative-id":["10.1145\/3627673.3679628","10.1145\/3627673"],"URL":"https:\/\/doi.org\/10.1145\/3627673.3679628","relation":{},"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"2024-10-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}