Beihong Ji

702 total citations
33 papers, 507 citations indexed

About

Beihong Ji is a scholar working on Molecular Biology, Computational Theory and Mathematics and Pharmacology. According to data from OpenAlex, Beihong Ji has authored 33 papers receiving a total of 507 indexed citations (citations by other indexed papers that have themselves been cited), including 22 papers in Molecular Biology, 20 papers in Computational Theory and Mathematics and 7 papers in Pharmacology. Recurrent topics in Beihong Ji's work include Computational Drug Discovery Methods (20 papers), Protein Structure and Dynamics (10 papers) and Pharmacogenetics and Drug Metabolism (7 papers). Beihong Ji is often cited by papers focused on Computational Drug Discovery Methods (20 papers), Protein Structure and Dynamics (10 papers) and Pharmacogenetics and Drug Metabolism (7 papers). Beihong Ji collaborates with scholars based in United States, China and United Kingdom. Beihong Ji's co-authors include Junmei Wang, Viet Hoang Man, Xibing He, Xiang‐Qun Xie, Shuhan Liu, Jingchen Zhai, Phuong H. Nguyen, Philippe Derreumaux, Tai‐Sung Lee and Darrin M. York and has published in prestigious journals such as Nature Communications, SHILAP Revista de lepidopterología and Advanced Functional Materials.

In The Last Decade

Beihong Ji

30 papers receiving 498 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Beihong Ji United States 12 360 169 91 65 43 33 507
Yuri V. Mezentsev Russia 14 326 0.9× 78 0.5× 164 1.8× 44 0.7× 73 1.7× 58 596
Anand Balupuri South Korea 13 218 0.6× 161 1.0× 40 0.4× 24 0.4× 55 1.3× 52 425
Patrik Johansson Sweden 15 409 1.1× 113 0.7× 55 0.6× 55 0.8× 141 3.3× 20 641
Jingxuan Zhu China 12 309 0.9× 69 0.4× 28 0.3× 35 0.5× 20 0.5× 27 513
Olafur Gudmundsson United States 15 231 0.6× 53 0.3× 28 0.3× 84 1.3× 34 0.8× 24 724
Sally Rose United Kingdom 8 292 0.8× 172 1.0× 26 0.3× 56 0.9× 50 1.2× 12 510
Billy J. Williams‐Noonan Australia 11 272 0.8× 152 0.9× 14 0.2× 65 1.0× 53 1.2× 15 464
Changdev G. Gadhe South Korea 15 340 0.9× 170 1.0× 32 0.4× 17 0.3× 48 1.1× 48 608
Chayan Acharya United States 11 330 0.9× 146 0.9× 20 0.2× 34 0.5× 62 1.4× 16 611
Lydia Siragusa Italy 13 284 0.8× 169 1.0× 21 0.2× 24 0.4× 93 2.2× 22 451

Countries citing papers authored by Beihong Ji

Since Specialization
Citations

This map shows the geographic impact of Beihong Ji'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 Beihong Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Beihong Ji more than expected).

Fields of papers citing papers by Beihong Ji

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Beihong Ji. 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 Beihong Ji. The network helps show where Beihong Ji may publish in the future.

Co-authorship network of co-authors of Beihong Ji

This figure shows the co-authorship network connecting the top 25 collaborators of Beihong Ji. A scholar is included among the top collaborators of Beihong Ji 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 Beihong Ji. Beihong Ji is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Long, Jing, et al.. (2025). Auxiliary Discrminator Sequence Generative Adversarial Networks for Few Sample Molecule Generation. Journal of Chemical Information and Modeling. 65(19). 10311–10322.
2.
Huang, Haozhe, Yixian Huang, Beihong Ji, et al.. (2025). Rational development of gemcitabine-based nanoplatform for targeting SERPINB9/Granzyme B axis to overcome chemo-immune-resistance. Nature Communications. 16(1). 4176–4176. 3 indexed citations
3.
4.
5.
Huang, Haozhe, Beihong Ji, Yixian Huang, et al.. (2024). Advanced Hierarchical Computational Modeling‐Based Rational Development of Platinum (II) Nanocomplex to Improve Lung Cancer Therapy. Advanced Functional Materials. 35(7). 4 indexed citations
6.
Zhai, Jingchen, et al.. (2023). Comparison and summary of in silico prediction tools for CYP450-mediated drug metabolism. Drug Discovery Today. 28(10). 103728–103728. 22 indexed citations
7.
Ji, Beihong, et al.. (2023). Predicting anti-SARS-CoV-2 activities of chemical compounds using machine learning models. SHILAP Revista de lepidopterología. 1(2). 100029–100029. 2 indexed citations
8.
Ge, Haixia, et al.. (2023). Discovery of Potent and Selective CB2 Agonists Utilizing a Function-Based Computational Screening Protocol. ACS Chemical Neuroscience. 14(21). 3941–3958. 3 indexed citations
9.
Ming, Yue, Chunyuan Luo, Beihong Ji, & Jian Cheng. (2023). ARPC5 acts as a potential prognostic biomarker that is associated with cell proliferation, migration and immune infiltrate in gliomas. BMC Cancer. 23(1). 937–937. 3 indexed citations
10.
Zhai, Jingchen, et al.. (2022). In Silico Prediction of Pharmacokinetic Profile for Human Oral Drug Candidates Which Lack Clinical Pharmacokinetic Experiment Data. European Journal of Drug Metabolism and Pharmacokinetics. 47(3). 403–417. 2 indexed citations
11.
Zhai, Jingchen, et al.. (2022). Physiologically-Based Pharmacokinetics Modeling for Hydroxychloroquine as a Treatment for Malaria and Optimized Dosing Regimens for Different Populations. Journal of Personalized Medicine. 12(5). 796–796. 4 indexed citations
12.
Zhai, Jingchen, Xibing He, Zhuoya Wan, et al.. (2022). In silico binding affinity prediction for metabotropic glutamate receptors using both endpoint free energy methods and a machine learning-based scoring function. Physical Chemistry Chemical Physics. 24(30). 18291–18305. 3 indexed citations
13.
He, Xibing, et al.. (2021). In silico binding profile characterization of SARS-CoV-2 spike protein and its mutants bound to human ACE2 receptor. Briefings in Bioinformatics. 22(6). 22 indexed citations
14.
Guo, Xinfeng, Clayton A. Wiley, Richard A. Steinman, et al.. (2021). Aicardi-Goutières syndrome-associated mutation at ADAR1 gene locus activates innate immune response in mouse brain. Journal of Neuroinflammation. 18(1). 169–169. 35 indexed citations
15.
Ji, Beihong, Xibing He, Jingchen Zhai, et al.. (2021). Incorporating structural similarity into a scoring function to enhance the prediction of binding affinities. Journal of Cheminformatics. 13(1). 11–11. 4 indexed citations
16.
Ming, Yue, Guang Xin, Beihong Ji, et al.. (2020). Entecavir as a P2X7R antagonist ameliorates platelet activation and thrombus formation. Journal of Pharmacological Sciences. 144(1). 43–51. 10 indexed citations
17.
Ji, Beihong, Shuhan Liu, Ying Xue, et al.. (2019). Prediction of Drug–Drug Interactions Between Opioids and Overdosed Benzodiazepines Using Physiologically Based Pharmacokinetic (PBPK) Modeling and Simulation. Drugs in R&D. 19(3). 297–305. 13 indexed citations
18.
Ji, Beihong. (2019). Pharmacokinetics Modeling and Molecular Modeling of Drug-Drug Interactions Between Opioids and Benzodiazepines. D-Scholarship@Pitt (University of Pittsburgh). 1 indexed citations
19.
He, Xibing, Viet Hoang Man, Beihong Ji, Xiang‐Qun Xie, & Junmei Wang. (2018). Calculate protein–ligand binding affinities with the extended linear interaction energy method: application on the Cathepsin S set in the D3R Grand Challenge 3. Journal of Computer-Aided Molecular Design. 33(1). 105–117. 29 indexed citations
20.
Ji, Beihong, et al.. (2002). Research progress on the bioactivities and the action modes of neem-based pesticides. Senlin bingchong tongxun. 21(6). 23–28. 1 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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