Dejun Jiang

2.7k total citations · 1 hit paper
54 papers, 1.7k citations indexed

About

Dejun Jiang is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Dejun Jiang has authored 54 papers receiving a total of 1.7k indexed citations (citations by other indexed papers that have themselves been cited), including 38 papers in Molecular Biology, 36 papers in Computational Theory and Mathematics and 18 papers in Materials Chemistry. Recurrent topics in Dejun Jiang's work include Computational Drug Discovery Methods (36 papers), Protein Structure and Dynamics (19 papers) and Machine Learning in Materials Science (18 papers). Dejun Jiang is often cited by papers focused on Computational Drug Discovery Methods (36 papers), Protein Structure and Dynamics (19 papers) and Machine Learning in Materials Science (18 papers). Dejun Jiang collaborates with scholars based in China, Macao and Hong Kong. Dejun Jiang's co-authors include Tingjun Hou, Dongsheng Cao, Zhenhua Wu, Chang‐Yu Hsieh, Jike Wang, Zhe Wang, Chao Shen, Ben Liao, Guangyong Chen and Yu Kang and has published in prestigious journals such as Nature Communications, Bioinformatics and Journal of Medicinal Chemistry.

In The Last Decade

Dejun Jiang

48 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

2021 388 citations Dejun Jiang, Zhenhua Wu et al. Journal of Cheminformatics profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Dejun Jiang China	20	1.1k	914	634	140	109	54	1.7k
Dingyan Wang China	22	1.2k 1.1×	1.2k 1.3×	761 1.2×	141 1.0×	97 0.9×	47	2.1k
Feisheng Zhong China	13	1.1k 1.0×	952 1.0×	671 1.1×	123 0.9×	111 1.0×	18	1.7k
Zhaoping Xiong China	13	1.1k 1.0×	828 0.9×	647 1.0×	164 1.2×	121 1.1×	22	1.6k
Youjun Xu China	13	873 0.8×	849 0.9×	446 0.7×	98 0.7×	94 0.9×	20	1.6k
Xutong Li China	20	1.2k 1.1×	1.1k 1.2×	715 1.1×	160 1.1×	111 1.0×	67	2.0k
Chao Shen China	25	1.4k 1.3×	1.4k 1.5×	619 1.0×	92 0.7×	185 1.7×	81	2.3k
Djork-Arné Clevert Germany	18	1.1k 1.0×	1.5k 1.6×	765 1.2×	197 1.4×	88 0.8×	38	2.4k
Marcus Olivecrona Sweden	3	1.5k 1.3×	1.1k 1.2×	980 1.5×	203 1.4×	123 1.1×	3	2.1k
Nikolaus Stiefl Switzerland	20	779 0.7×	742 0.8×	393 0.6×	104 0.7×	123 1.1×	43	1.5k
Daniel P. Russo United States	20	733 0.7×	509 0.6×	378 0.6×	98 0.7×	83 0.8×	36	1.5k

Countries citing papers authored by Dejun Jiang

Since Specialization

Citations

This map shows the geographic impact of Dejun Jiang's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dejun Jiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dejun Jiang more than expected).

Fields of papers citing papers by Dejun Jiang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dejun Jiang. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dejun Jiang. The network helps show where Dejun Jiang may publish in the future.

Co-authorship network of co-authors of Dejun Jiang

This figure shows the co-authorship network connecting the top 25 collaborators of Dejun Jiang. A scholar is included among the top collaborators of Dejun Jiang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dejun Jiang. Dejun Jiang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Zeng, Tao, Fei Xia, Dejun Jiang, et al.. (2025). TarIKGC: A Target Identification Tool Using Semantics-Enhanced Knowledge Graph Completion with Application to CDK2 Inhibitor Discovery. Journal of Medicinal Chemistry. 68(2). 1793–1809.

Zhai, Silong, Jike Wang, Stephanie Lin, et al.. (2025). PepPCBench is a Comprehensive Benchmarking Framework for Protein–Peptide Complex Structure Prediction. Journal of Chemical Information and Modeling. 65(16). 8497–8513. 4 indexed citations

Yi, Jiacai, Dejun Jiang, Chengkun Wu, et al.. (2025). Pushing the boundaries of few-shot learning for low-data drug discovery with a Bayesian meta-learning hypernetwork framework. Briefings in Bioinformatics. 26(4).

Wang, Xiaorui, Xiaodan Yin, Xujun Zhang, et al.. (2025). A virtual platform for automated hybrid organic-enzymatic synthesis planning. Nature Communications. 16(1). 10929–10929.

Fu, Xiangzheng, et al.. (2024). Predicting novel targets with Bayesian machine learning by integrating multiple biological signatures. Chemical Science. 15(35). 14471–14484. 3 indexed citations

Li, Shuai, Jike Wang, Odin Zhang, et al.. (2024). ClickGen: Directed exploration of synthesizable chemical space via modular reactions and reinforcement learning. Nature Communications. 15(1). 10127–10127. 10 indexed citations

Jiang, Dejun, Zhe Wang, Huiyong Sun, et al.. (2024). TransfIGN: A Structure-Based Deep Learning Method for Modeling the Interaction between HLA-A*02:01 and Antigen Peptides. Journal of Chemical Information and Modeling. 64(13). 5016–5027. 2 indexed citations

Jiang, Dejun, Yanan Zhou, Hong‐Xia Jiang, et al.. (2024). Exploring Gene Expression and Alternative Splicing in Duck Embryonic Myoblasts via Full-Length Transcriptome Sequencing. Veterinary Sciences. 11(12). 601–601.

Wang, Xiaorui, Xiaodan Yin, Dejun Jiang, et al.. (2024). Multi-modal deep learning enables efficient and accurate annotation of enzymatic active sites. Nature Communications. 15(1). 7348–7348. 20 indexed citations

10.

Du, Hongyan, Dejun Jiang, Odin Zhang, et al.. (2023). A flexible data-free framework for structure-based de novo drug design with reinforcement learning. Chemical Science. 14(43). 12166–12181. 10 indexed citations

11.

Wu, Zhenhua, Jike Wang, Hongyan Du, et al.. (2023). Chemistry-intuitive explanation of graph neural networks for molecular property prediction with substructure masking. Nature Communications. 14(1). 89 indexed citations

12.

Jiang, Dejun, et al.. (2023). Landslide Monitoring along the Dadu River in Sichuan Based on Sentinel-1 Multi-Temporal InSAR. Sensors. 23(7). 3383–3383. 10 indexed citations

13.

Li, Shengming, Jintu Zhang, Zhe Wang, et al.. (2023). SDEGen: learning to evolve molecular conformations from thermodynamic noise for conformation generation. Chemical Science. 14(6). 1557–1568. 19 indexed citations

14.

Jiang, Dejun, Chang‐Yu Hsieh, Ziyi Yang, et al.. (2023). MetalProGNet: a structure-based deep graph model for metalloprotein–ligand interaction predictions. Chemical Science. 14(8). 2054–2069. 17 indexed citations

15.

Li, Yuquan, Chang‐Yu Hsieh, Xiaoqing Gong, et al.. (2022). An adaptive graph learning method for automated molecular interactions and properties predictions. Nature Machine Intelligence. 4(7). 645–651. 34 indexed citations

16.

Hu, Xueping, Jinping Pang, Changwei Chen, et al.. (2022). Discovery of novel non-steroidal selective glucocorticoid receptor modulators by structure- and IGN-based virtual screening, structural optimization, and biological evaluation. European Journal of Medicinal Chemistry. 237. 114382–114382. 20 indexed citations

17.

Wang, Zhenyang, et al.. (2021). Predictive modelling for contact angle of liquid metals and oxide ceramics by comparing Gaussian process regression with other machine learning methods. Ceramics International. 48(1). 665–673. 18 indexed citations

18.

Jiang, Dejun, Zhenhua Wu, Chang‐Yu Hsieh, et al.. (2021). Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models. Journal of Cheminformatics. 13(1). 12–12. 388 indexed citations breakdown →

19.

Hu, Xueping, Xin Chai, Md Shah Alam, et al.. (2021). Discovery of novel DprE1 inhibitors via computational bioactivity fingerprints and structure-based virtual screening. Acta Pharmacologica Sinica. 43(6). 1605–1615. 13 indexed citations

20.

Gou, Deming, et al.. (2001). Construction and characterization of a cDNA library from 4-week-old human embryo. Gene. 278(1-2). 141–147. 13 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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