{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T17:14:35Z","timestamp":1775841275943,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","funder":[{"name":"National Natural Science Foundation of China","award":["62462007"],"award-info":[{"award-number":["62462007"]}]},{"name":"National Natural Science Foundation of China","award":["62302023"],"award-info":[{"award-number":["62302023"]}]},{"name":"Guangxi Natural Science Foundation","award":["2025JJA170297"],"award-info":[{"award-number":["2025JJA170297"]}]},{"name":"2025 Guangxi Normal University Joint Training Project of the National Natural Science Foundation","award":["2025PY41"],"award-info":[{"award-number":["2025PY41"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,4,13]]},"DOI":"10.1145\/3774904.3792118","type":"proceedings-article","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T21:54:34Z","timestamp":1775771674000},"page":"571-579","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Graph Diffusion Evolution Model for Multi-Conditional Molecular Generation"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4643-8126","authenticated-orcid":false,"given":"Xingcheng","family":"Fu","sequence":"first","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Guangxi Normal University, Guilin, China and Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6768-4011","authenticated-orcid":false,"given":"Lingyun","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Guangxi Normal University, Guilin, China and Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7564-5171","authenticated-orcid":false,"given":"Yisen","family":"Gao","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0649-5799","authenticated-orcid":false,"given":"Tianyu","family":"Chen","sequence":"additional","affiliation":[{"name":"SKLCCSE, School of Computer Science and Engineering, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1930-3848","authenticated-orcid":false,"given":"Qingyun","family":"Sun","sequence":"additional","affiliation":[{"name":"SKLCCSE, School of Computer Science and Engineering, Beihang University, Beijing, China and Guangxi Key Lab of Multi-source Information Mining & Security, Guangxi Normal University, Guilin, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5152-0055","authenticated-orcid":false,"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[{"name":"SKLCCSE, School of Computer Science and Engineering, Beihang University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7083-3847","authenticated-orcid":false,"given":"Xianxian","family":"Li","sequence":"additional","affiliation":[{"name":"Key Lab of Education Blockchain and Intelligent Technology, Guangxi Normal University, Guilin, China"}]}],"member":"320","published-online":{"date-parts":[[2026,4,12]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00839"},{"key":"e_1_3_2_1_2_1","volume-title":"International conference on machine learning. PMLR, 5722-5747","author":"Choi Jeongwhan","year":"2023","unstructured":"Jeongwhan Choi, Seoyoung Hong, Noseong Park, and Sung-Bae Cho. 2023. Gread: Graph neural reaction-diffusion networks. In International conference on machine learning. PMLR, 5722-5747."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/fpsyg.2023.1162655"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.7b00622"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-024-00843-5"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1186\/1758-2946-1-8"},{"key":"e_1_3_2_1_7_1","volume-title":"Hyperbolic geometric latent diffusion model for graph generation. arXiv preprint arXiv:2405.03188","author":"Fu Xingcheng","year":"2024","unstructured":"Xingcheng Fu, Yisen Gao, Yuecen Wei, Qingyun Sun, Hao Peng, Jianxin Li, and Xianxian Li. 2024. Hyperbolic geometric latent diffusion model for graph generation. arXiv preprint arXiv:2405.03188 (2024)."},{"key":"e_1_3_2_1_8_1","volume-title":"Xingang Peng, Yufeng Su, Jian Peng, and Jianzhu Ma.","author":"Guan Jiaqi","year":"2023","unstructured":"Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, and Jianzhu Ma. 2023. 3d equivariant diffusion for target-aware molecule generation and affinity prediction. arXiv preprint arXiv:2303.03543 (2023)."},{"key":"e_1_3_2_1_9_1","volume-title":"Learning on Graphs Conference. PMLR, 33-1.","author":"Hua Chenqing","year":"2024","unstructured":"Chenqing Hua, Sitao Luan, Minkai Xu, Zhitao Ying, Jie Fu, Stefano Ermon, and Doina Precup. 2024. Mudiff: Unified diffusion for complete molecule generation. In Learning on Graphs Conference. PMLR, 33-1."},{"key":"e_1_3_2_1_10_1","volume-title":"A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets. bioRxiv","author":"Huang Lei","year":"2023","unstructured":"Lei Huang, Tingyang Xu, Yang Yu, Peilin Zhao, Ka-Chun Wong, and Hengtong Zhang. 2023a. A dual diffusion model enables 3D binding bioactive molecule generation and lead optimization given target pockets. bioRxiv (2023), 2023-01."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25639"},{"key":"e_1_3_2_1_12_1","volume-title":"Retrobridge: Modeling retrosynthesis with markov bridges. arXiv preprint arXiv:2308.16212","author":"Igashov Ilia","year":"2023","unstructured":"Ilia Igashov, Arne Schneuing, Marwin Segler, Michael Bronstein, and Bruno Correia. 2023. Retrobridge: Modeling retrosynthesis with markov bridges. arXiv preprint arXiv:2308.16212 (2023)."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-024-00815-9"},{"key":"e_1_3_2_1_14_1","volume-title":"A graph-based genetic algorithm and generative model\/Monte Carlo tree search for the exploration of chemical space. Chemical science","author":"Jensen Jan H","year":"2019","unstructured":"Jan H Jensen. 2019. A graph-based genetic algorithm and generative model\/Monte Carlo tree search for the exploration of chemical space. Chemical science, Vol. 10, 12 (2019), 3567-3572."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28700"},{"key":"e_1_3_2_1_16_1","volume-title":"International conference on machine learning. PMLR, 2323-2332","author":"Jin Wengong","year":"2018","unstructured":"Wengong Jin, Regina Barzilay, and Tommi Jaakkola. 2018. Junction tree variational autoencoder for molecular graph generation. In International conference on machine learning. PMLR, 2323-2332."},{"key":"e_1_3_2_1_17_1","volume-title":"International conference on machine learning. PMLR, 10362-10383","author":"Jo Jaehyeong","year":"2022","unstructured":"Jaehyeong Jo, Seul Lee, and Sung Ju Hwang. 2022. Score-based generative modeling of graphs via the system of stochastic differential equations. In International conference on machine learning. PMLR, 10362-10383."},{"key":"e_1_3_2_1_18_1","volume-title":"Anuroop Sriram, and Zachary W Ulissi.","author":"Joshi Chaitanya K","year":"2025","unstructured":"Chaitanya K Joshi, Xiang Fu, Yi-Lun Liao, Vahe Gharakhanyan, Benjamin Kurt Miller, Anuroop Sriram, and Zachary W Ulissi. 2025. All-atom diffusion transformers: Unified generative modelling of molecules and materials. arXiv preprint arXiv:2503.03965 (2025)."},{"key":"e_1_3_2_1_19_1","volume-title":"International Conference on Machine Learning. PMLR","author":"Lee Seul","year":"2023","unstructured":"Seul Lee, Jaehyeong Jo, and Sung Ju Hwang. 2023. Exploring chemical space with score-based out-of-distribution generation. In International Conference on Machine Learning. PMLR, 18872-18892."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btad222"},{"key":"e_1_3_2_1_21_1","volume-title":"Generative diffusion models on graphs: Methods and applications. arXiv preprint arXiv:2302.02591","author":"Liu Chengyi","year":"2023","unstructured":"Chengyi Liu, Wenqi Fan, Yunqing Liu, Jiatong Li, Hang Li, Hui Liu, Jiliang Tang, and Qing Li. 2023a. Generative diffusion models on graphs: Methods and applications. arXiv preprint arXiv:2302.02591 (2023)."},{"key":"e_1_3_2_1_22_1","first-page":"8065","article-title":"Graph diffusion transformers for multi-conditional molecular generation","volume":"37","author":"Liu Gang","year":"2024","unstructured":"Gang Liu, Jiaxin Xu, Tengfei Luo, and Meng Jiang. 2024. Graph diffusion transformers for multi-conditional molecular generation. Advances in Neural Information Processing Systems, Vol. 37 (2024), 8065-8092.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_23_1","volume-title":"Molxpt: Wrapping molecules with text for generative pre-training. arXiv preprint arXiv:2305.10688","author":"Liu Zequn","year":"2023","unstructured":"Zequn Liu, Wei Zhang, Yingce Xia, Lijun Wu, Shufang Xie, Tao Qin, Ming Zhang, and Tie-Yan Liu. 2023b. Molxpt: Wrapping molecules with text for generative pre-training. arXiv preprint arXiv:2305.10688 (2023)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2023.3344758"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28738"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.3c01496"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphar.2020.565644"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1021\/acs.jcim.8b00234"},{"key":"e_1_3_2_1_29_1","volume-title":"Gp-molformer: A foundation model for molecular generation. arXiv preprint arXiv:2405.04912","author":"Ross Jerret","year":"2024","unstructured":"Jerret Ross, Brian Belgodere, Samuel C Hoffman, Vijil Chenthamarakshan, Jiri Navratil, Youssef Mroueh, and Payel Das. 2024. Gp-molformer: A foundation model for molecular generation. arXiv preprint arXiv:2405.04912 (2024)."},{"key":"e_1_3_2_1_30_1","volume-title":"The Twelfth International Conference on Learning Representations.","author":"Song Yuxuan","year":"2024","unstructured":"Yuxuan Song, Jingjing Gong, Hao Zhou, Mingyue Zheng, Jingjing Liu, and Wei-Ying Ma. 2024. Unified generative modeling of 3d molecules with bayesian flow networks. In The Twelfth International Conference on Learning Representations."},{"key":"e_1_3_2_1_31_1","volume-title":"A Unified Invariant Learning Framework for Graph Classification. arXiv preprint arXiv:2501.12595","author":"Sui Yongduo","year":"2025","unstructured":"Yongduo Sui, Jie Sun, Shuyao Wang, Zemin Liu, Qing Cui, Longfei Li, and Xiang Wang. 2025. A Unified Invariant Learning Framework for Graph Classification. arXiv preprint arXiv:2501.12595 (2025)."},{"key":"e_1_3_2_1_32_1","volume-title":"Digress: Discrete denoising diffusion for graph generation. arXiv preprint arXiv:2209.14734","author":"Vignac Clement","year":"2022","unstructured":"Clement Vignac, Igor Krawczuk, Antoine Siraudin, Bohan Wang, Volkan Cevher, and Pascal Frossard. 2022. Digress: Discrete denoising diffusion for graph generation. arXiv preprint arXiv:2209.14734 (2022)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-43415-0_33"},{"key":"e_1_3_2_1_34_1","volume-title":"Chemical Science","volume":"16","author":"Wang Jike","year":"2024","unstructured":"Jike Wang, Hao Luo, Rui Qin, Mingyang Wang, Xiaozhe Wan, Meijing Fang, Odin Zhang, Qiaolin Gou, Qun Su, and Chao Shen. 2024. 3DSMILES-GPT: 3D molecular pocket-based generation with token-only large language model. Chemical Science, Vol. 16, 2 (2024)."},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.3390\/molecules28114430"},{"key":"e_1_3_2_1_36_1","volume-title":"MoleculeNet: a benchmark for molecular machine learning. Chemical science","author":"Wu Zhenqin","year":"2018","unstructured":"Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. 2018. MoleculeNet: a benchmark for molecular machine learning. Chemical science, Vol. 9, 2 (2018), 513-530."},{"key":"e_1_3_2_1_37_1","volume-title":"Mars: Markov molecular sampling for multi-objective drug discovery. arXiv preprint arXiv:2103.10432","author":"Xie Yutong","year":"2021","unstructured":"Yutong Xie, Chence Shi, Hao Zhou, Yuwei Yang, Weinan Zhang, Yong Yu, and Lei Li. 2021. Mars: Markov molecular sampling for multi-objective drug discovery. arXiv preprint arXiv:2103.10432 (2021)."},{"key":"e_1_3_2_1_38_1","volume-title":"International Conference on Machine Learning. PMLR, 38592-38610","author":"Xu Minkai","year":"2023","unstructured":"Minkai Xu, Alexander S Powers, Ron O Dror, Stefano Ermon, and Jure Leskovec. 2023. Geometric latent diffusion models for 3d molecule generation. In International Conference on Machine Learning. PMLR, 38592-38610."},{"key":"e_1_3_2_1_39_1","first-page":"79704","article-title":"Discrete-state continuous-time diffusion for graph generation","volume":"37","author":"Xu Zhe","year":"2024","unstructured":"Zhe Xu, Ruizhong Qiu, Yuzhong Chen, Huiyuan Chen, Xiran Fan, Menghai Pan, Zhichen Zeng, Mahashweta Das, and Hanghang Tong. 2024. Discrete-state continuous-time diffusion for graph generation. Advances in Neural Information Processing Systems, Vol. 37 (2024), 79704-79740.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1021\/ma300213b"},{"key":"e_1_3_2_1_41_1","first-page":"10238","article-title":"Graph denoising diffusion for inverse protein folding","volume":"36","author":"Yi Kai","year":"2023","unstructured":"Kai Yi, Bingxin Zhou, Yiqing Shen, Pietro Li\u00f2, and Yuguang Wang. 2023. Graph denoising diffusion for inverse protein folding. Advances in Neural Information Processing Systems, Vol. 36 (2023), 10238-10257.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-025-01035-y"},{"key":"e_1_3_2_1_43_1","volume-title":"A survey on graph diffusion models: Generative ai in science for molecule, protein and material. arXiv preprint arXiv:2304.01565","author":"Zhang Mengchun","year":"2023","unstructured":"Mengchun Zhang, Maryam Qamar, Taegoo Kang, Yuna Jung, Chenshuang Zhang, Sung-Ho Bae, and Chaoning Zhang. 2023. A survey on graph diffusion models: Generative ai in science for molecule, protein and material. arXiv preprint arXiv:2304.01565 (2023)."},{"key":"e_1_3_2_1_44_1","volume-title":"Jianmin Bao, Shaozhe Hao, Lu Yuan, and Kwan Yee K. Wong.","author":"Zhao Shihao","year":"2023","unstructured":"Shihao Zhao, Dongdong Chen, Yen Chun Chen, Jianmin Bao, Shaozhe Hao, Lu Yuan, and Kwan Yee K. Wong. 2023. Uni-ControlNet: All-in-One Control to Text-to-Image Diffusion Models. (2023)."}],"event":{"name":"WWW '26: The ACM Web Conference 2026","location":"Dubai United Arab Emirates","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2026"],"original-title":[],"deposited":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:32:51Z","timestamp":1775838771000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774904.3792118"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,12]]},"references-count":44,"alternative-id":["10.1145\/3774904.3792118","10.1145\/3774904"],"URL":"https:\/\/doi.org\/10.1145\/3774904.3792118","relation":{},"subject":[],"published":{"date-parts":[[2026,4,12]]},"assertion":[{"value":"2026-04-12","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}