{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T01:51:15Z","timestamp":1765504275284,"version":"3.48.0"},"publisher-location":"New York, NY, USA","reference-count":42,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 62192784, U22B2038, 62472329"],"award-info":[{"award-number":["No. 62192784, U22B2038, 62472329"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Young Elite Scientists Sponsorship Program by CAST","award":["No.2023QNRC001"],"award-info":[{"award-number":["No.2023QNRC001"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,10]]},"DOI":"10.1145\/3746252.3761352","type":"proceedings-article","created":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T00:29:28Z","timestamp":1762561768000},"page":"2967-2976","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Full-Atom Protein-Protein Interaction Prediction via Atomic Equivariant Attention Network"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5778-9490","authenticated-orcid":false,"given":"Chunchen","family":"Wang","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China and Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7821-0030","authenticated-orcid":false,"given":"Cheng","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3194-1690","authenticated-orcid":false,"given":"Wenchuan","family":"Yang","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, Changsha, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9655-2787","authenticated-orcid":false,"given":"Le","family":"Song","sequence":"additional","affiliation":[{"name":"Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, USA and GenBio AI, Palo Alto, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3734-0266","authenticated-orcid":false,"given":"Chuan","family":"Shi","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jctc.7b00125"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539441"},{"key":"e_1_3_2_1_3_1","volume-title":"Struct2Graph: a graph attention network for structure based predictions of protein-protein interactions. BMC bioinformatics","author":"Baranwal Mayank","year":"2022","unstructured":"Mayank Baranwal, Abram Magner, Jacob Saldinger, Emine S Turali-Emre, Paolo Elvati, Shivani Kozarekar, J Scott VanEpps, Nicholas A Kotov, Angela Violi, and Alfred O Hero. 2022. Struct2Graph: a graph attention network for structure based predictions of protein-protein interactions. BMC bioinformatics, Vol. 23, 1 (2022), 1-28."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3119569"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btz184"},{"key":"e_1_3_2_1_6_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_7_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-023-36736-1"},{"key":"e_1_3_2_1_8_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_9_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_10_1","volume-title":"Acute myeloid leukemia originates from a hierarchy of leukemic stem cell classes that differ in self-renewal capacity. Nature immunology","author":"Hope Kristin J","year":"2004","unstructured":"Kristin J Hope, Liqing Jin, and John E Dick. 2004. Acute myeloid leukemia originates from a hierarchy of leukemic stem cell classes that differ in self-renewal capacity. Nature immunology, Vol. 5, 7 (2004), 738-743."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.csbj.2022.06.025"},{"key":"e_1_3_2_1_12_1","volume-title":"Equivariant graph mechanics networks with constraints. arXiv preprint arXiv:2203.06442","author":"Huang Wenbing","year":"2022","unstructured":"Wenbing Huang, Jiaqi Han, Yu Rong, Tingyang Xu, Fuchun Sun, and Junzhou Huang. 2022. Equivariant graph mechanics networks with constraints. arXiv preprint arXiv:2203.06442 (2022)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty635"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-12201-9"},{"key":"e_1_3_2_1_15_1","volume-title":"International Conference on Machine Learning. PMLR, 10217-10227","author":"Jin Wengong","year":"2022","unstructured":"Wengong Jin, Regina Barzilay, and Tommi Jaakkola. 2022. Antibody-antigen docking and design via hierarchical structure refinement. In International Conference on Machine Learning. PMLR, 10217-10227."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1101\/2023.12.10.570461"},{"key":"e_1_3_2_1_17_1","volume-title":"Raphael JL Townshend, and Ron Dror","author":"Jing Bowen","year":"2020","unstructured":"Bowen Jing, Stephan Eismann, Patricia Suriana, Raphael JL Townshend, and Ron Dror. 2020. Learning from protein structure with geometric vector perceptrons. arXiv preprint arXiv:2009.01411 (2020)."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.93.1.13"},{"key":"e_1_3_2_1_19_1","first-page":"583","volume-title":"Nature","volume":"596","author":"Jumper John","year":"2021","unstructured":"John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin \u017d\u00eddek, Anna Potapenko, et al., 2021. Highly accurate protein structure prediction with AlphaFold. Nature, Vol. 596, 7873 (2021), 583-589."},{"volume-title":"Graph Neural Networks: Foundations, Frontiers, and Applications","author":"Kabir Anowarul","key":"e_1_3_2_1_20_1","unstructured":"Anowarul Kabir and Amarda Shehu. 2022. Graph neural networks in predicting protein function and interactions. In Graph Neural Networks: Foundations, Frontiers, and Applications. Springer, 541-556."},{"key":"e_1_3_2_1_21_1","volume-title":"Conditional antibody design as 3d equivariant graph translation. arXiv preprint arXiv:2208.06073","author":"Kong Xiangzhe","year":"2022","unstructured":"Xiangzhe Kong, Wenbing Huang, and Yang Liu. 2022. Conditional antibody design as 3d equivariant graph translation. arXiv preprint arXiv:2208.06073 (2022)."},{"key":"e_1_3_2_1_22_1","volume-title":"End-to-End Full-Atom Antibody Design. arXiv preprint arXiv:2302.00203","author":"Kong Xiangzhe","year":"2023","unstructured":"Xiangzhe Kong, Wenbing Huang, and Yang Liu. 2023. End-to-End Full-Atom Antibody Design. arXiv preprint arXiv:2302.00203 (2023)."},{"key":"e_1_3_2_1_23_1","unstructured":"Julia Koehler Leman Brian D Weitzner Steven M Lewis Jared Adolf-Bryfogle Nawsad Alam Rebecca F Alford Melanie Aprahamian David Baker Kyle A Barlow Patrick Barth et al. 2020. Macromolecular modeling and design in Rosetta: recent methods and frameworks. Nature methods Vol. 17 7 (2020) 665-680."},{"key":"e_1_3_2_1_24_1","volume-title":"Rotamer Density Estimator is an Unsupervised Learner of the Effect of Mutations on Protein-Protein Interaction. bioRxiv","author":"Luo Shitong","year":"2023","unstructured":"Shitong Luo, Yufeng Su, Zuofan Wu, Chenpeng Su, Jian Peng, and Jianzhu Ma. 2023. Rotamer Density Estimator is an Unsupervised Learner of the Effect of Mutations on Protein-Protein Interaction. bioRxiv (2023), 2023-02."},{"key":"e_1_3_2_1_25_1","volume-title":"Learning unknown from correlations: graph neural network for inter-novel-protein interaction prediction. arXiv preprint arXiv:2105.06709","author":"Lv Guofeng","year":"2021","unstructured":"Guofeng Lv, Zhiqiang Hu, Yanguang Bi, and Shaoting Zhang. 2021. Learning unknown from correlations: graph neural network for inter-novel-protein interaction prediction. arXiv preprint arXiv:2105.06709 (2021)."},{"key":"e_1_3_2_1_26_1","first-page":"29287","article-title":"Language models enable zero-shot prediction of the effects of mutations on protein function","volume":"34","author":"Meier Joshua","year":"2021","unstructured":"Joshua Meier, Roshan Rao, Robert Verkuil, Jason Liu, Tom Sercu, and Alex Rives. 2021. Language models enable zero-shot prediction of the effects of mutations on protein function. Advances in Neural Information Processing Systems, Vol. 34 (2021), 29287-29303.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_27_1","volume-title":"International Conference on Machine Learning. PMLR, 16990-17017","author":"Notin Pascal","year":"2022","unstructured":"Pascal Notin, Mafalda Dias, Jonathan Frazer, Javier Marchena Hurtado, Aidan N Gomez, Debora Marks, and Yarin Gal. 2022. Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval. In International Conference on Machine Learning. PMLR, 16990-17017."},{"key":"e_1_3_2_1_28_1","volume-title":"Frame averaging for invariant and equivariant network design. arXiv preprint arXiv:2110.03336","author":"Puny Omri","year":"2021","unstructured":"Omri Puny, Matan Atzmon, Heli Ben-Hamu, Ishan Misra, Aditya Grover, Edward J Smith, and Yaron Lipman. 2021. Frame averaging for invariant and equivariant network design. arXiv preprint arXiv:2110.03336 (2021)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1101\/2021.02.12.430858"},{"key":"e_1_3_2_1_30_1","volume-title":"International conference on machine learning. PMLR, 9323-9332","author":"Satorras Victor Garcia","year":"2021","unstructured":"Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling. 2021. E (n) equivariant graph neural networks. In International conference on machine learning. PMLR, 9323-9332."},{"key":"e_1_3_2_1_31_1","volume-title":"Matthew IJ Raybould, and Charlotte M Deane","author":"Schneider Constantin","year":"2022","unstructured":"Constantin Schneider, Matthew IJ Raybould, and Charlotte M Deane. 2022. SAbDab in the age of biotherapeutics: updates including SAbDab-nano, the nanobody structure tracker. Nucleic acids research, Vol. 50, D1 (2022), D1368-D1372."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.2122954119"},{"key":"e_1_3_2_1_33_1","volume-title":"Protein-protein interaction prediction with deep learning: A comprehensive review. Computational and Structural Biotechnology Journal","author":"Soleymani Farzan","year":"2022","unstructured":"Farzan Soleymani, Eric Paquet, Herna Viktor, Wojtek Michalowski, and Davide Spinello. 2022. Protein-protein interaction prediction with deep learning: A comprehensive review. Computational and Structural Biotechnology Journal (2022)."},{"key":"e_1_3_2_1_34_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_35_1","volume-title":"Proceedings of ICLR 2018","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. Proceedings of ICLR 2018 (2017)."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1039\/C7MB00188F"},{"key":"e_1_3_2_1_37_1","volume-title":"Atomic protein structure refinement using all-atom graph representations and SE (3)-equivariant graph neural networks. bioRxiv","author":"Wu Tianqi","year":"2022","unstructured":"Tianqi Wu and Jianlin Cheng. 2022. Atomic protein structure refinement using all-atom graph representations and SE (3)-equivariant graph neural networks. bioRxiv (2022), 2022-05."},{"key":"e_1_3_2_1_38_1","volume-title":"How powerful are graph neural networks? arXiv preprint arXiv:1810.00826","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)."},{"key":"e_1_3_2_1_39_1","volume-title":"Graph-based prediction of Protein-protein interactions with attributed signed graph embedding. BMC bioinformatics","author":"Yang Fang","year":"2020","unstructured":"Fang Yang, Kunjie Fan, Dandan Song, and Huakang Lin. 2020. Graph-based prediction of Protein-protein interactions with attributed signed graph embedding. BMC bioinformatics, Vol. 21, 1 (2020), 1-16."},{"key":"e_1_3_2_1_40_1","volume-title":"Hussein A Abbas, Michelle Chan-Seng-Yue, Veronique Voisin, Peter van Galen, Anne Tierens, et al.","author":"Zeng Andy GX","year":"2022","unstructured":"Andy GX Zeng, Suraj Bansal, Liqing Jin, Amanda Mitchell, Weihsu Claire Chen, Hussein A Abbas, Michelle Chan-Seng-Yue, Veronique Voisin, Peter van Galen, Anne Tierens, et al., 2022. A cellular hierarchy framework for understanding heterogeneity and predicting drug response in acute myeloid leukemia. Nature medicine, Vol. 28, 6 (2022), 1212-1223."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.omtn.2020.08.025"},{"key":"e_1_3_2_1_42_1","volume-title":"Wai Leong Tam, and Xiaoli Li","author":"Zhao Ziyuan","year":"2023","unstructured":"Ziyuan Zhao, Peisheng Qian, Xulei Yang, Zeng Zeng, Cuntai Guan, Wai Leong Tam, and Xiaoli Li. 2023. SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction. arXiv preprint arXiv:2305.08316 (2023)."}],"event":{"name":"CIKM '25: The 34th ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Seoul Republic of Korea","acronym":"CIKM '25"},"container-title":["Proceedings of the 34th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746252.3761352","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T01:47:02Z","timestamp":1765504022000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746252.3761352"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":42,"alternative-id":["10.1145\/3746252.3761352","10.1145\/3746252"],"URL":"https:\/\/doi.org\/10.1145\/3746252.3761352","relation":{},"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"2025-11-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}