基本信息：
蔣德軍，男，博士,中南大學(xué)湘雅藥學(xué)院特聘副教授,碩士研究生導(dǎo)師,中共黨員。1994年10月生，重慶開州人。2017畢業(yè)于中國藥科大學(xué),獲學(xué)士學(xué)位，2022年畢業(yè)于浙江大學(xué)，獲博士學(xué)位。蔣德軍博士長期致力于人工智能藥學(xué)和化學(xué)信息學(xué)等交叉領(lǐng)域的研究。近五年在Nature Machine Intelligence、NatureComputationalScience、NatureCommunications、Chemical Science、Journal of Medicinal Chemistry和Research等國際高水平期刊發(fā)表論文40余篇,其中以第一作者或者通訊作者（含共同）在Chemical Science、Journal of Medicinal Chemistry、Research和Journal of Chemical Theory and Computation等權(quán)威期刊發(fā)表論文逾20篇。其研究成果廣受學(xué)術(shù)界關(guān)注，其中兩篇第一作者研究論文被引用分別逾700次（J Cheminform 13, 12 (2021)）與250次（J. Med. Chem. 2021, 64, 24, 18209–18232），Google Scholar統(tǒng)計總被引逾2616余次，H-index指數(shù)23。蔣德軍博士同時擔(dān)任NatureCommunications、JournalofCheminformatics、Briefings in Bioinformatics等國際知名期刊審稿人，積極參與國際學(xué)術(shù)共同體建設(shè)與同行評議工作。主持2025重大新藥創(chuàng)制子課題、國家自然科學(xué)基金青年項目、中國博士后科學(xué)基金面上項目（二等）、湖南省自然科學(xué)基金青年項目等多項科研項目，并成功入選2023年國家資助博士后研究人員計劃（B檔）。曾榮獲“浙江大學(xué)優(yōu)秀研究生”、“三好研究生”、“優(yōu)秀畢業(yè)研究生”等多項榮譽稱號。
研究方向：
1.人工智能藥學(xué)
2.計算機輔助藥物設(shè)計
教育與工作經(jīng)歷：
2024-07至今,中南大學(xué)，湘雅藥學(xué)院，特聘副教授，碩士研究生導(dǎo)師
2022-06至2024-07，浙江大學(xué)智能創(chuàng)新藥物研究院，博士后/助理研究員(合作導(dǎo)師：侯廷軍教授)
2020-09至2022-06,浙江大學(xué),計算機技術(shù),博士(導(dǎo)師：吳健教授、侯廷軍教授)
2017-09至2020-06,浙江大學(xué),藥學(xué),其他
2013-09至2017-06,中國藥科大學(xué),信息管理與信息系統(tǒng),學(xué)士
科研項目與資助：
（1）國家自然基金青年項目，資助金額：30萬元，起止時間：2024.01 – 2026.12，主持
（2）中國博士后面上基金（二等），資助金額：8萬元，起止時間：2022.10 – 2024.10，主持
（3）2023年國家資助博士后研究人員計劃（B檔），36萬元，主持，起止時間：2022.10 – 2024.10
（4）湖南省青年基金項目，5萬元，2025.06– 2028.06，主持
（5）2025“創(chuàng)新藥物研發(fā)”國家科技重大專項（子課題負責(zé)人），140萬元，2026.01–2028.12， 主持
谷歌學(xué)術(shù)鏈接：
https://scholar.google.com/citations?user=B1J94LwAAAAJ&hl=zh-CN
聯(lián)系方式:jiang_dj@zju.edu.cn
近5年代表性科研論文：
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區(qū),中科院1區(qū))
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區(qū),中科院1區(qū)，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區(qū),中科院1區(qū))
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區(qū),中科院1區(qū))
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區(qū),中科院1區(qū))
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區(qū),中科院2區(qū),谷歌學(xué)術(shù)他引700次，截至2025.12)
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區(qū),中科院1區(qū),IF5=7.2,谷歌學(xué)術(shù)他引248次，截至2025.12)
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區(qū),中科院2區(qū))
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.Chem. Sci., 2023,14, 12166-12181(JCR1區(qū),中科院1區(qū),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區(qū))
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區(qū),中科院2區(qū))
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區(qū))
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. (JCR1區(qū))
14. Cuiyu Li, Hongyan Du, Chengwei Zhang, Wanying Huang, Xujun Zhang, Tianyue Wang,Dejun Jiang*, Tingjun Hou*, and Ercheng Wang*.Comprehensive Evaluation of End-Point Free Energy Methods in DNA–Ligand Interaction Predictions.Journal of Chemical Information and Modeling2025 65 (4), 2014-2025.(JCR1區(qū))
15.Kaimo Yang#,Dejun Jiang#, Qirui Deng, Sutong Xiang, Jingxuan Ge, Kexin Xu, Zhiliang Jiang, Zihao Wang, Chen Yin, Youqiao Qian, Tingjun Hou, Huiyong Sun.A Unified Deep Graph Model for Identifying the Molecular Categories of Ligands Targeting Nuclear Receptors.J. Chem. Inf. Model. 2025, 65, 11, 5481–5494(JCR1區(qū))
16.Jian-Wang Li, Ke-yi Liu, You-Chao Deng, Shao-Hua Shi, Xiang-Zheng Fu, Yue-Ping Jiang, Jing Fang,De-Jun Jiang*, Shao Liu*, Dong-Sheng Cao*.Uncertainty-Aware Deep Learning Modeling and Structural Feature Insights for Nephrotoxicity Prediction.J. Chem. Inf. Model.(JCR1區(qū))
17.Kun Li, Jiacai Yi, Qing Ye, Xixi Yang, Long Yu, YouChao Deng, Chengkun Wu, Tingjun Hou*,Dejun Jiang*, Dongsheng Cao*. A fused deep learning approach to transform drug repositioning.Commun Chem8, 334 (2025).(中科院1區(qū))
18. Yue Li, Jiacai Yi, Hui Li, Kun Li, Youchao Deng, Chengkun Wu, Xiangzheng Fu,Dejun Jiang*, Dongsheng Cao*. Decoding the Limits of Deep Learning in Drug Discovery: Benchmarking AI-Driven Molecular Docking.Chem. Sci., 2025, 16, 17374-17390（中科院1區(qū)，NI指數(shù)）
19.Dejun Jiang#, Huifeng Zhao#, Hongyan Du#, Yu Kang, Peichen Pan, Zhenxing Wu, Yundian Zeng, Odin Zhang, Xiaorui Wang, Jike Wang, YuanSheng Huang, Yihao Zhao, Chang-Yu Hsieh*, Dongsheng Cao*, Huiyong Sun*, Tingjun Hou*. Harnessing Deep Statistical Potential for Biophysical Scoring of Protein-peptide Interactions.Acta Pharmacol Sin (2025).https://doi.org/10.1038/s41401-025-01659-8（中科院2區(qū)）
