基本信息：
蒋德军，男，中共党员，1994年10月生，重庆开州人。2017毕业于中国药科大学,获学士学位。2022年毕业于浙江大学，获博士学位。主要从事人工智能药学和化学信息学等交叉领域的研究。近五年以第一作者或者通讯作者（含共同）在Chemical Science, Journal of Medicinal Chemistry, Journal of Chemical Theory and Computation等国际知名期刊发表论文15余篇,以共同作者在期刊Nature Machine Intelligence、Chemical Science、Journal of Medicinal Chemistry等期刊发表论文20余篇。研究成果被广泛引用，其中以第一作者发表论文单篇研究性论文最高引用近600次，Google Scholar统计总被引近2000余次，H-index指数18。担任NatureCommunications、JournalofCheminformatics、Briefings in Bioinformatics等国际知名期刊审稿人。主持国家自然科学基金青年基金和中国博士后面上基金（二等）各一项,湖南省青年基金一项、获得2023年国家资助博士后研究人员计划（B档）。曾获得浙江大学优秀研究生、三好研究生、优秀毕业研究生等称号。
研究方向：
1.人工智能药学
2.计算机辅助药物设计
教育与工作经历：
2024-07至今,中南大学，湘雅药学院，特聘副教授
2022-06至2024-07，浙江大学智能创新药物研究院，博士后/助理研究员(合作导师：侯廷军教授)
2020-09至2022-06,浙江大学,计算机技术,博士(导师：吴健教授、侯廷军教授)
2017-09至2020-06,浙江大学,药学,其他
2013-09至2017-06,中国药科大学,信息管理与信息系统,学士
科研项目与资助：
（1）国家自然基金青年项目，资助金额：30万元，起止时间：2024.01 – 2026.12，主持
（2）中国博士后面上基金（二等），资助金额：8万元，起止时间：2022.10 – 2024.10，主持
（3）2023年国家资助博士后研究人员计划（B档）,36万元（共两年），主持
（4）湖南省青年基金项目，5万元，2025.04 – 2027.04，主持
谷歌学术链接：
https://scholar.google.com/citations?user=B1J94LwAAAAJ&hl=zh-CN
联系方式:jiang_dj@zju.edu.cn
近5年代表性科研论文：
1.Jiang, D.;Zhao, H.; Du, H.; Deng, Y.; Wu, Z.; Wang, J.; Zeng, Y.; Zhang, H.; Wang, X.; Wu, J.; Hsieh, C. Y.; Hou, T., How Good Are Current Docking Programs at Nucleic Acid–Ligand Docking? A Comprehensive Evaluation.Journal of Chemical Theory and Computation2023, 19, 5633-5647.(JCR1区,中科院1区)
2.Jiang, D.;Ye, Z.; Hsieh, C.-Y.; Yang, Z.; Zhang, X.; Kang, Y.; Du, H.; Wu, Z.; Wang, J.; Zeng, Y.; Zhang, H.; Wang, X.; Wang, M.; Yao, X.; Zhang, S.; Wu, J.; Hou, T. MetalProGNet: a structure-based deep graph model for metalloprotein–ligand interaction predictions.Chemical Science2023, 14, 2054-2069.(JCR1区,中科院1区，NatureIndex期刊)
3.Jiang, D.#; Sun, H.#; Wang, J.#; Hsieh, C.-Y.; Li, Y.; Wu, Z.; Cao, D.; Wu, J.; Hou, T., Out-of-the-box deep learning prediction of quantum-mechanical partial charges by graph representation and transfer learning.Briefings in Bioinformatics2022, 23, bbab597.(JCR1区,中科院1区)
4. Du, H.#;Jiang, D.#; Gao, J.; Zhang, X.; Jiang, L.; Zeng, Y.; Wu, Z.; Shen, C.; Xu, L.; Cao, D., Proteome-Wide Profiling of the Covalent-Druggable Cysteines with a Structure-Based Deep Graph Learning Network.Research2022.(JCR1区,中科院1区)
5. Wu, Z.#;Jiang, D.#; Hsieh, C.-Y.; Chen, G.; Liao, B.; Cao, D.; Hou, T., Hyperbolic relational graph convolution networks plus: a simple but highly efficient QSAR-modeling method.Briefings in Bioinformatics2021, 22, bbab112.(JCR1区,中科院1区)
6.Jiang, D.#; Wu, Z.#; Hsieh, C.-Y.; Chen, G.; Liao, B.; Wang, Z.; Shen, C.; Cao, D.; Wu, J.; Hou, T., Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models.Journal of cheminformatics2021, 13, 1-23.(JCR1区,中科院2区,谷歌学术他引594次，截至2025.4)
7.Jiang, D.;Hsieh, C.-Y.; Wu, Z.; Kang, Y.; Wang, J.; Wang, E.; Liao, B.; Shen, C.; Xu, L.; Wu, J.; Cao, D.; Hou, T., InteractionGraphNet: a novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions.Journal of medicinal chemistry2021, 64, 18209-18232.(JCR1区,中科院1区,谷歌学术他引191次，截至2025.4)
8.Jiang, D.; Lei, T.; Wang, Z.; Shen, C.; Cao, D.; Hou, T., ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning.Journal of Cheminformatics2020, 12, 1-26.(JCR1区,中科院2区,谷歌学术他引75次，截至2025.4)
9. Hongyan Du#;Dejun Jiang#; Haotian Zhang; Zhenxing Wu; Junbo Gao; Xujun Zhang; Xiaorui Wang; Yafeng Deng; Yu Kang; Dan Li; Peichen Pan; Chang-Yu Hsieh; Tingjun Hou. A Flexible Data-Free Framework for Structure Based De Novo Drug Design with ReinforcementLearning.Chemical Science. (DOI: 10.1039/d3sc04091g)(JCR1区,中科院1区,NatureIndex期刊)
10.Dejun Jiang;Hongyan Du; Huifeng Zhao; Yafeng Deng; Zhenxing Wu; Jike Wang; Yundian Zeng; Haotian Zhang; Xiaorui Wang; Ercheng Wang; Tingjun Hou; Chang-Yu Hsieh. Assessing the Performance of MM/PBSA and MM/GBSA Methods. 10. Prediction Reliability of Binding Affinities and Binding Poses for RNA-ligand Complexes.Physical Chemistry Chemical Physics.2024,26, 10323-10335.(JCR1区)
11. Jingxuan Ge#,Dejun Jiang#,Huiyong Sun#,YuKang, Peichen Pan, Yafeng Deng, Chang-Yu Hsieh, Tingjun Hou.Deep-learning-based prediction framework for protein-peptide interactions with structure generation pipeline.Cell Reports Physical ScienceVolume 5, Issue 6, 101980, June 19, 2024.(JCR1区)
12.Huifeng Zhao#,Dejun Jiang#, Chao Shen,Jintu Zhang, Xujun Zhang, Xiaorui Wang, Dou Nie, Yu Kang, Tingjun Hou. Comprehensive Evaluation of Ten Docking Programs on a Diverse Set of Protein-cyclic Peptide Complexes.Journal of Chemical Information and Modeling.2024, 64, 6, 2112–2124.(JCR1区)
13. Nanqi Hong#，Dejun Jiang#，TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides.Journal of Chemical Information and Modeling2024, 64, 13, 5016–5027.
14. Cuiyu Li, Hongyan Du, Chengwei Zhang, Wanying Huang, Xujun Zhang, Tianyue Wang, Dejun Jiang*, Tingjun Hou*, and Ercheng Wang*.Journal of Chemical Information and Modeling 2025 65 (4), 2014-2025.(JCR1区)
