机器学习和生物信息学实验室联盟

标题: 药物信息学研究工作与成果 [打印本页]

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作者: zouquan    时间: 2022-7-18 20:29
标题: 药物信息学研究工作与成果
基金项目

• 面向药物副作用识别的多视角稀疏模型研究，62172076，面上，丁漪杰主持
• 基于三维结构的小分子靶向药物智能发现与通用架构设计研究，62472069，面上，Stalin Antony主持
• 基于推荐算法与多源数据类型的药物-靶标相互作用预测，32470693，面上，茹晓青主持
• 基于多类特征整合与排序学习的药物-靶标相互作用预测，62101094，青年，茹晓青主持
• 基于单细胞测序数据的药物反应预测研究，62201129，青年，齐忍主持
• 基于生物医学复杂网络与多模态联合表征学习的药物-药物相互作用预测研究，62503371，青年，任忠豪主持
  
  
  

研究成员

• 邹权（电子科技大学，教授）
• 鱼亮（西安电子科技大学，教授）
• 苏苒（天津大学，教授）
• 丁漪杰（电子科技大学长三角研究院（衢州），研究员）
• 齐忍（北京理工大学，副研究员）
• Stalin Antony（电子科技大学，助理研究员）
• 茹晓青（电子科技大学长三角研究院（衢州），实验员、科研助理）
• 任忠豪
• 陈瑶佳
  
  
  

发表论文
药物靶标相互作用

• Yifang Shi, Lin Gao, Quan Zou, Liang Yu. Prediction of drug-target interactionsbased on multi-layer network representation learning. Neurocomputing. 2021, 434: 80-89
• Xiaoqing Ru, Xiucai Ye*, Tetsuya Sakurai, Quan Zou, Lei Xu, Chen Lin*. Current status and future prospects of drug-target interactionprediction. Briefings in Functional Genomics. 2021, 20(5):312-322
• Yijie Ding, Jijun Tang, Fei Guo*, Quan Zou*. Identification of drug-target interactions viamultiple kernel-based triple collaborative matrix factorization. Briefings in Bioinformatics. 2022, 23(2): bbab582 (data and codes)
• Xiaoqing Ru, Xiucai Ye*,Tetsuya Sakurai, Quan Zou*. NerLTR-DTA: Drug-target binding affinityprediction based on neighbor relationship and learning to rank. Bioinformatics.2022, 38(7): 1964-1971. (codes and datasets)
• Xiaoqing Ru, Quan Zou, Chen Lin*. Optimization of drug-target affinity prediction methodsthrough feature processing schemes. Bioinformatics. 2023, 39(11):btad615 (codes)
• Hongjie Wu, Junkai Liu,Tengsheng Jiang, Quan Zou, Shujie Qi, Zhiming Cui, Prayag Tiwari*, YijieDing*. AttentionMGT-DTA: A multi-modal drug-target affinity prediction using graph transformer and attention mechanism. Neural Networks. 2024, 169: 623-636  (codes)
• Zhecheng Zhou,Qingquan Liao, Jinhang Wei, Linlin Zhuo*, Xiaonan Wu*, Xiangzheng Fu*, Quan Zou*. Revisiting Drug-Protein Interaction Prediction: A Novel Global-Local Perspective. Bioinformatics. 2024, 40(5): btae271. (codes)
• Wei Song, Lewen Xu, Chenguang Han, Zhen Tian*, Quan Zou*. Drug-target interactionpredictions with multi-view similarity network fusion strategy and deepinteractive attention mechanism. Bioinformatics.2024, 40(6): btae346. (codes)
• Zhen Tian, Yue Yu, Fengming Ni*, Quan Zou. Drug‑target interaction prediction with collaborative contrastive learning and adaptive self‑paced sampling strategy. BMC Biology. 2024, 22: 216
• Qian Liao, Yu Zhang, Ying Chu, Yi Ding, Zhen Liu,Xianyi Zhao, Yizheng Wang, Jie Wan, Yijie Ding*, Prayag Tiwari*, Quan Zou*, KeHan*. Application of Artificial Intelligence in Drug-Target InteractionsPrediction: A Review. npj Biomedical Innovations. 2025, 2:1
• Zhen Tian, Zhuangzhuang Zhang, Wanning Zhou, Zhixia Teng, Wei Song*, Quan Zou*. DSANIB: Drug-Target Interaction Predictions withDual-View Synergistic Attention Network and Information Bottleneck Strategy. IEEE Journal of Biomedical and Health Informatics. 2025, 29(2): 1484-1493
• Jiayue Hu, Yuhang Liu,Xiangxiang Zeng, Quan Zou, Ran Su, Leyi Wei. Multi-modal deep representation learning accurately identifies andinterprets drug-target interactions. IEEE Journal of Biomedical and Health Informatics. 2025, 29(7): 5350-5360
• Yuqing Qian, Xin Zhang,Yizheng Wang, Quan Zou, Chen Cao*, Yijie Ding*, Xiaoyi Guo*. DTI-RME: aRobust and Multi-Kernel Ensemble Approach for Drug-Target InteractionPrediction. BMC Biology. 2025, 23: 225
• Xiaoqing Ru, Lifeng Xu,Wu Han*, Quan Zou*. In silico methods for drug-target interactionprediction. Cell Reports Methods. 2025, 5: 101184
• Gaoming Lin, Xin Zhang,Zhonghao Ren, Quan Zou, Prayag Tiwari*, Changjun Zhou*, Yijie Ding*. TAPB:an interventional debiasing framework for alleviating target prior bias indrug-target interaction prediction. Nature Communications. 2025, 16:10867
• An Xiong, Zhenjie Luo,Yan Xia, Quan Zou, Leyi Wei, ZilongZhang*, Tao Wang*, Lesong Wei*, Feifei Cui*. An interpretable geometric graphneural network for enhancing the generalizability of drug–target interaction prediction. BMC Biology. 2025, 23:350.
• Yike Wang, Jingwei Lv,Yan Xia, Junlin Xu, Yajie Meng, Feifei Cui, Leyi Wei, Quan Zou, ZilongZhang*. A unified survey on drug-target interaction and binding affinityprediction: Models, representations, and challenges. Biotechnology Advances.2026, 88: 108843
• Yongqing Zhang, LeChen, Hong Luo, Tianhao Li, Shuwen Xiong, Zixuan Wang, Quan Zou, Wenqian Zhang*. Contrastive learning in both structureand function spaces improve drug-target interaction prediction. BMC Bioinformatics. 2026, 27: 55
  
  

药物药物相互作用

• Yaojia Chen, Jiacheng Wang, Quan Zou,Mengting Niu, Yijie Ding, Jiangning Song*, Yansu Wang*. DrugDAGT: adual-attention graph transformer with contrastive learning improves drug-drug interaction prediction. BMC Biology. 2024, 22: 233 (codes)
• Yan Xia, An Xiong, Zilong Zhang*, Quan Zou*, Feifei Cui*. A comprehensive review of deep learning-based approaches for drug–drug interaction prediction. Briefings in Functional Genomics. 2025, 24, elae052
• Zhonghao Ren, XiangxiangZeng, Yizhen Lao, Zhuhong You, Yifan Shang, Quan Zou*, Chen Lin*. Predicting rare drug-drug interaction eventswith dual-granular structure-adaptive and pair variational representation. Nature Communications. 2025, 16: 3997. (codes)
• Xiaoqing Ru, Zhen Li, LeyiWei, Yuanan Liu, Quan Zou*. Drug-Druginteraction prediction: paradigm shifts, key bottlenecks, and future directions.Wiley Interdisciplinary Reviews: Computational Molecular Science. 2025, 15: e70056
• Mengli Li, Chao Cao, Quan Zou, Leyi Wei, Yansu Wang*. MHAFR-DDI:a multimodal hierarchical attention fusion and relation-aware architecture fordrug–drug interaction event prediction. Briefingsin Bioinformatics. 2026, 27(1): bbag077
  
  

药物与RNA

• Yaojia Chen, Jiacheng Wang, Chunyu Wang, Quan Zou*. AutoEdge-CCP:A Novel Approach for Predicting Cancer-Associated CircRNAs and Drugs based on Automated Edge Embedding. PLoS Computational Biology. 2024, 20(1): e1011851
• Zhecheng Zhou, Zhenya Du, Xin Jiang, Linlin Zhuo*, Yixin Xu,Xiangzheng Fu*, Mingzhe Liu, Quan Zou*. GAM-MDR: probing miRNA–drug resistance using a graph autoencoder based on random path masking. Briefings in Functional Genomics. 2024, 23(4): 475-483. (codes)
• Zhen Li, Qingquan Liao, Wenbin Liu*, Peng Xu,Linlin Zhuo, Xiangzheng Fu, Quan Zou. Multi-source data integration forexplainable miRNA-driven drug discovery. Future Generation Computer Systems.2024, 160: 109-119. (codes)
• Qingyu Wang, Suwan Mao,Xiaoyan Liu, Quan Zou, Qing Liu, XiSu, Junjie Wang*, Wen Gao*, Ximei Luo*. BridgeSyn: a bridging fusion frameworkfor drug combination synergy prediction. Briefingsin Bioinformatics. 2025, 26(6): bbaf624
  
  

药物重定位

• Junkai Liu, Shixuan Guan, Quan Zou, Hongjie Wu*,Prayag Tiwari, Yijie Ding. AMDGT: Attention aware multi-modal fusion using adual graph transformer for drug–disease associations prediction. Knowledge-Based Systems. 2024, 284: 111359
• Junkai Liu, Fuyuan Hu, Quan Zou, PrayagTiwari*, Hongjie Wu*, Yijie Ding*. Drug repositioning by multi-aspectheterogeneous graph contrastive learning and positive-fusion negative samplingstrategy. Information Fusion. 2024, 112: 102563
• Jinhang Wei, Linlin Zhuo*, Xiangzheng Fu*, XiangxiangZeng, Li Wang, Quan Zou, Dongsheng Cao*.DrugReAlign: a multisource prompt framework for drug repurposing based on largelanguage models. BMC Biology. 2024, 22:226 (codes)
• 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, 7: 1413 (codes)
  
  

药物发现

• Shunyun Yang, Runxin Guo, Rui Liu, Xiangke Liao, Quan Zou*,Benyun Shi*, Shaoliang Peng*. cmFSM: A Scalable CPU-MIC Coordinated Drug-Finding Tool by Frequent Subgraph Mining. BMC Bioinformatics.2018, 19(Suppl4): 98 (codes)
• Yu Cheng, Yongshun Gong, Yuansheng Liu*, Bosheng Song*, Quan Zou. Molecular design in drug discovery: a comprehensivereview of deep generative models. Briefings in Bioinformatics. 2021, 22(6):bbab34.
• Leyi Wei, Quan Zou, Xiangxiang Zeng. Editorial: Artificial Intelligence in Drug Discovery and Development. Methods. 2024, 226: 133-137
• Jiangyan Zhang, HaolinLi, Yuncong Zhang, Junyang Huang, Liping Ren, Chuantao Zhang*, Quan Zou*, Yang Zhang*. ComputationalToxicology in Drug Discovery: Applications of Artificial Intelligence in ADMETand Toxicity Prediction. Briefings in Bioinformatics. 2025, 26(5): bbaf533
• Meiqi Fan, Quan Zou*. From Protein Structure to Drug Discovery: Bioinformatics Breakthroughs in 2024–2025. Current issues in molecular biology. 2026, 48(1): 33
  
  

药物毒副作用预测

• Ran Su, Haitang Yang, Leyi Wei*, Siqi Chen*, Quan Zou*. A multi-label learning model for predicting drug-induced pathology in multi-organ based on toxicogenomics data. PLoS Computational Biology. 2022, 18(9): e1010402. (codes)
• Yuqing Qian, Yijie Ding*, Quan Zou*, Fei Guo*. Identification of drug-side effect association via restricted Boltzmann machines with penalizedterm. Briefings in Bioinformatics. 2022,23(6): bbac458
• Zhonghao Ren, ZhuhongYou*, Quan Zou*, Changqing Yu*, Yanfang Ma*, Yongjian Guan, Hairu You, XinfeiWang, Jie Pan. DeepMPF: deep learning framework for predicting drug–target interactions based on multi-modal representation with meta-path semanticanalysis. Journal of Translational Medicine. 2023, 21: 48 (web server)
• Yijie Ding, Hongmei Zhou, Quan Zou*, Lei Yuan*. Identification of Drug-side Effect Association via Correntropy-loss based Matrix Factorization with Neural Tangent Kernel. Methods. 2023, 219: 73-81
• Yijie Ding, Fei Guo,Prayag Tiwari*, Quan Zou*. Identification of Drug-Side Effect AssociationVia Multi-View Semi-Supervised Sparse Model. IEEE Transactions on Artificial Intelligence.
• Xinqian Ma, Xiangzheng Fu, Tao Wang, Linlin Zhuo*,Quan Zou*. GraphADT: Empowering Interpretable Predictions of Acute Dermal Toxicity with Multi-View Graph Pooling and Structure Remapping. Bioinformatics.2024, 40(7): btae438. (codes)
• Yuqing Qian, ZiyuZheng, Prayag Tiwari*, Yijie Ding*, Quan Zou. Multiple Kronecker RLSfusion-based link propagation for drug-side effect prediction. Transactionson Machine Learning Research. 2024, 2407.00105.
  
  

其他

• Ran Su, Xinyi Liu, Leyi Wei, Quan Zou*. Deep-Resp-Forest: Adeep forest model to predict anti-cancer drug response. Methods. 2019, 166: 91-102
• Xiaoqing Ru, Lida Wang*, Lihong Li, Hui Ding, Xiucai Ye, Quan Zou*.Exploration of the Correlation between GPCRs and Drugs Based on a Learning to Rank Algorithm. Computers in Biology and Medicine. 2020, 119: 103660
• Liang Yu, Yayong Shi, Quan Zou, ShuhangWang, Liping Zheng, Lin Gao. Exploring drug treatment patterns based on theaction of drug and multilayer network model. International Journal of Molecular Sciences. 2020, 21: 5014
• Yuxin Gong, Bo Liao*, Peng Wang, Quan Zou. DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins. Frontiers in Pharmacology. 2021, 12: 771808
• Ren Qi, Quan Zou*.Trends and Potential of Machine Learning and Deep Learning in Drug Study at Single-cell Level. Research. 2023,6: 0050
• Ren Qi, Quan Zou*. Editorial: MachineLearning Methods in Single-Cell Immune and Drug Response Prediction. Frontiersin Genetics. 2023, 19: 1233078
• Chao Pang, Jianbo Qiao,Xiangxiang Zeng, Quan Zou, Leyi Wei*. Deep Generative Models in De Novo Drug Molecule Generation. Journal of Chemical Information and Modeling. 2024, 64(7): 2174-2194
• Zhecheng Zhou, Linlin Zhuo*, Xiangzheng Fu*, Quan Zou*. Joint Deep Autoencoder and Subgraph Augmentation for Inferring Microbial Responses to Drugs. Briefings in Bioinformatics. 2024, 25(1): bbad483. (codes)
• Xiaoqing Ru, Shulin Zhao, Quan Zou, Lifeng Xu*. Identifypotential drug candidates within a high-quality compound search space. Briefings in Bioinformatics. 2025, 26(1):bbaf024
• Xingyue Gu, Yue Yu,Junkai Liu, Pengfeng Xiao*, Quan Zou*,Xiaoyi Guo*, Xin Zhang*, Yijie Ding*. CMMSCL-DPI: cross-modal multi-structuralcontrastive learning for predicting drug-protein interactions. BMC Biology. 2026, 24: 69
  
  

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