📝 Publications

†: Equal contribution ✉: Corresponding author

Peer-reviewed Conference

Hongxin Xiang, Ke Li, Mingquan Liu, Zhixiang Cheng, Bin Yao, Wenjie Du, Jun Xia, Li Zeng, Xin Jin✉, Xiangxiang Zeng✉, “EDBench: Large-Scale Electron Density Data for Molecular Modeling”. Advances in Neural Information Processing Systems (NeurIPS) 2025. [pdf] [project page] [github] [database]

Xuan Lin, Qingrui Liu, Hongxin Xiang✉, Daojian Zeng, Xiangxiang Zeng, “Enhancing Chemical Reaction and Retrosynthesis Prediction with Large Language Model and Dual-task Learning”. In Proceedings of the 34th International Joint Conference on Artificial Intelligence. 2025. [arXiv] [github]

Hongxin Xiang, Jun Xia, Xin Jin, Wenjie Du, Li Zeng, Xiangxiang Zeng✉, “Electron Density-enhanced Molecular Geometry Learning”. In Proceedings of the 34th International Joint Conference on Artificial Intelligence. 2025. [github]

Jun Xia, Sizhe Liu, Jingbo Zhou, Shaorong Chen, Hongxin Xiang, Zicheng Liu, Yue Liu, Stan Z. Li, “Bridging the Gap between Database Search and De Novo Peptide Sequencing with SearchNovo”. In Proceedings of The Thirteenth International Conference on Learning Representations. 2025. [paper] [github]

Shuai Zhang, Junfeng Fang, Xuqiang Li, Hongxin Xiang, Jun Xia, Ye Wei, Wenjie Du, Yang Wang, “Iterative Substructure Extraction for Molecular Relational Learning with Interactive Graph Information Bottleneck”. In Proceedings of The Thirteenth International Conference on Learning Representations. 2025. [paper]

Sizhe Liu, Jun Xia, Lecheng Zhang, Yuchen Liu, Yue Liu, Wenjie Du, Zhangyang Gao, Bozhen Hu, Cheng Tan, Hongxin Xiang, Stan Z. Li, “FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning”, [paper] [arXiv] [github]

Hongxin Xiang, Shuting Jin, Jun Xia, Man Zhou, Jianmin Wang, Li Zeng, Xiangxiang Zeng, “An Image-enhanced Molecular Graph Representation Learning Framework”. In Proceedings of the 33th International Joint Conference on Artificial Intelligence. 2024. [paper] [github] [Poster] [PPT]

Peer-reviewed Journal

Hongxin Xiang, Mingquan Liu, Linlin Hou, Shuting Jin, Jianmin Wang, Jun Xia, Wenjie Du, Sisi Yuan, Xiangzheng Fu, Xinyu Yang, Li Zeng, and Lei Xu✉, “n Image-based Protein-Ligand Binding Representation Learning Framework via Multi-Level Flexible Dynamics Trajectory Pre-training”. Bioinformatics, 2025. [paper] [github]

Shuting Jin, Xiangrong Liu, Junlin Xu, Sisi Yuan, Hongxin Xiang, Lian Shen, Chunyan Li, Zhangming Niu, Yinhui Jiang, “Adaptive symmetry-based adversarial perturbation augmentation for molecular graph representations with dual-fusion attention information”. Information Fusion, 2025. [paper] [github]

Jianmin Wang, Jiashun Mao, Chunyan Li, Hongxin Xiang, Xun Wang, Shuang Wang, Zixu Wang, Yangyang Chen, Yuquan Li, Kyoung Tai No, Tao Song & Xiangxiang Zeng, “Interface-aware molecular generative framework for protein–protein interaction modulators”. Journal of Cheminformatics, 2024. [paper] [github]

Hongxin Xiang, Li Zeng, Linlin Hou, Kenli Li, Zhimin Fu, Yunguang Qiu, Ruth Nussinov, Jianying Hu, Michal Rosen-Zvi, Xiangxiang Zeng & Feixiong Cheng, “A molecular video-derived foundation model for scientific drug discovery”. Nature Communications, 2024. [paper] [github]

Zhonghao Ren, Xiangxiang Zeng, Yizhen Lao, Heping Zheng, Zhuhong You, Hongxin Xiang & Quan Zou, “A spatial hierarchical network learning framework for drug repositioning allowing interpretation from macro to micro scale”. Communications Biology, 2024. [paper] [github]

Linlin Hou†, Hongxin Xiang†, Xiangxiang Zeng, Dongsheng Cao, Li Zeng, Bosheng Song, “Attribute-guided prototype network for few-shot molecular property prediction”. Briefings in Bioinformatics, 2024. [paper] [github]

Xiang Zhang, Hongxin Xiang✉, Xixi Yang, Jingxin Dong, Xiangzheng Fu, Xiangxiang Zeng, Haowen Chen✉, Keqin Li, “Chemical structure-aware molecular image representation learning”. Dual-View Learning Based on Images and Sequences for Molecular Property Prediction, 2023. [paper] [github]

Hongxin Xiang, Shuting Jin, Xiangrong Liu, Xiangxiang Zeng, Li Zeng, “Chemical structure-aware molecular image representation learning”. Briefings in Bioinformatics, 2023. [paper] [github]

Xiangxiang Zeng†, Hongxin Xiang†, Linhui Yu, Jianmin Wang, Kenli Li, Ruth Nussinov & Feixiong Cheng, “Accurate prediction of molecular properties and drug targets using a self-supervised image representation learning framework”. Nature Machine Intelligence, 2022. [paper] [github]

Preprints & Under Submission

Zhixiang Cheng†, Hongxin Xiang†, Pengsen Ma, Li Zeng, Xin Jin, Xixi Yang, Jianxin Lin, Yang Deng, Bosheng Song, Xinxin Feng, Changhui Deng, Xiangxiang Zeng, “MaskMol: Knowledge-guided Molecular Image Pre-Training Framework for Activity Cliffs”. arXiv preprint arXiv:2505.09262. [paper]