Fuqiang Ban

4.8k total citations · 3 hit papers
64 papers, 3.0k citations indexed

About

Fuqiang Ban is a scholar working on Molecular Biology, Pulmonary and Respiratory Medicine and Computational Theory and Mathematics. According to data from OpenAlex, Fuqiang Ban has authored 64 papers receiving a total of 3.0k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Molecular Biology, 21 papers in Pulmonary and Respiratory Medicine and 18 papers in Computational Theory and Mathematics. Recurrent topics in Fuqiang Ban's work include Prostate Cancer Treatment and Research (21 papers), Computational Drug Discovery Methods (18 papers) and Estrogen and related hormone effects (15 papers). Fuqiang Ban is often cited by papers focused on Prostate Cancer Treatment and Research (21 papers), Computational Drug Discovery Methods (18 papers) and Estrogen and related hormone effects (15 papers). Fuqiang Ban collaborates with scholars based in Canada, United States and United Kingdom. Fuqiang Ban's co-authors include Artem Cherkasov, Anh‐Tien Ton, Francesco Gentile, Michael Hsing, Russell J. Boyd, Paul S. Rennie, Eric Leblanc, Tong He, Martin Ester and Michael Fernández and has published in prestigious journals such as Journal of the American Chemical Society, Journal of Biological Chemistry and Nature Communications.

In The Last Decade

Fuqiang Ban

64 papers receiving 2.9k 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.

Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds

2020 382 citations Anh‐Tien Ton, Francesco Gentile et al. Molecular Informatics profile →
Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery

2020 259 citations Francesco Gentile, Michael Hsing et al. ACS Central Science profile →
Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking

2022 218 citations Francesco Gentile, Michael Fernández et al. Nature Protocols profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fuqiang Ban Canada	28	1.6k	1.4k	459	441	416	64	3.0k
Jin Huang China	34	2.5k 1.6×	1.2k 0.9×	365 0.8×	637 1.4×	98 0.2×	164	4.4k
Xiaoqin Zou United States	30	3.1k 2.0×	1.8k 1.3×	545 1.2×	428 1.0×	265 0.6×	87	4.1k
Paul A. Rejto United States	29	1.7k 1.1×	713 0.5×	317 0.7×	276 0.6×	152 0.4×	68	2.7k
Daniel Seeliger Germany	24	2.5k 1.6×	798 0.6×	475 1.0×	386 0.9×	92 0.2×	43	3.8k
T. Dwight McGee United States	7	2.4k 1.5×	813 0.6×	313 0.7×	519 1.2×	84 0.2×	8	3.7k
Oleg Ursu United States	23	2.4k 1.5×	1.2k 0.9×	173 0.4×	453 1.0×	97 0.2×	51	3.8k
Michael J. Hartshorn United Kingdom	17	3.7k 2.3×	2.1k 1.5×	697 1.5×	910 2.1×	278 0.7×	21	5.5k
Peichen Pan China	28	2.1k 1.3×	1.1k 0.8×	360 0.8×	337 0.8×	205 0.5×	88	3.1k
Maria A. Miteva France	37	3.2k 2.0×	2.0k 1.4×	442 1.0×	728 1.7×	78 0.2×	129	5.2k
Teruki Honma Japan	31	1.8k 1.1×	701 0.5×	252 0.5×	724 1.6×	115 0.3×	124	3.5k

Countries citing papers authored by Fuqiang Ban

Since Specialization

Citations

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

Fields of papers citing papers by Fuqiang Ban

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fuqiang Ban

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

All Works

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20 of 20 papers shown

Gentile, Francesco, et al.. (2024). In silico screening of LRRK2 WDR domain inhibitors using deep docking and free energy simulations. Chemical Science. 15(23). 8800–8812. 12 indexed citations

Gentile, Francesco, et al.. (2024). PROTACable Is an Integrative Computational Pipeline of 3-D Modeling and Deep Learning To Automate the De Novo Design of PROTACs. Journal of Chemical Information and Modeling. 64(8). 3034–3046. 13 indexed citations

Foo, Jane, Francesco Gentile, Hélène Morin, et al.. (2024). Characterization of novel small molecule inhibitors of estrogen receptor-activation function 2 (ER-AF2). Breast Cancer Research. 26(1). 168–168. 3 indexed citations

Morin, Hélène, Mohit Pandey, Fuqiang Ban, et al.. (2023). Novel Inhibitors of androgen receptor's DNA binding domain identified using an ultra‐large virtual screening. Molecular Informatics. 42(8-9). e2300026–e2300026. 7 indexed citations

Gentile, Francesco, Michael Fernández, Anh‐Tien Ton, et al.. (2022). Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking. Nature Protocols. 17(3). 672–697. 218 indexed citations breakdown →

Song, Yi, Ahn R. Lee, Joseph Lee, et al.. (2021). Discovery of New Catalytic Topoisomerase II Inhibitors for Anticancer Therapeutics. Frontiers in Oncology. 10. 633142–633142. 41 indexed citations

Gentile, Francesco, Michael Hsing, Anh‐Tien Ton, et al.. (2020). Deep Docking: A Deep Learning Platform for Augmentation of Structure Based Drug Discovery. ACS Central Science. 6(6). 939–949. 259 indexed citations breakdown →

Chamberlain, Thomas C., Andrew Ming‐Lum, Edis Dzananovic, et al.. (2020). Interleukin-10 and Small Molecule SHIP1 Allosteric Regulators Trigger Anti-inflammatory Effects through SHIP1/STAT3 Complexes. iScience. 23(8). 101433–101433. 22 indexed citations

Lee, Jaeyong, L.J. Worrall, M. Vuckovic, et al.. (2020). Crystallographic structure of wild-type SARS-CoV-2 main protease acyl-enzyme intermediate with physiological C-terminal autoprocessing site. Nature Communications. 11(1). 5877–5877. 154 indexed citations

10.

Ton, Anh‐Tien, Francesco Gentile, Michael Hsing, Fuqiang Ban, & Artem Cherkasov. (2020). Rapid Identification of Potential Inhibitors of SARS‐CoV‐2 Main Protease by Deep Docking of 1.3 Billion Compounds. Molecular Informatics. 39(8). e2000028–e2000028. 382 indexed citations breakdown →

11.

Fernández, Michael, Fuqiang Ban, Carl F. Perez, et al.. (2019). Quantitative Structure–Price Relationship (QS$R) Modeling and the Development of Economically Feasible Drug Discovery Projects. Journal of Chemical Information and Modeling. 59(4). 1306–1313. 3 indexed citations

12.

Fernández, Michael, Fuqiang Ban, Michael Hsing, et al.. (2018). Toxic Colors: The Use of Deep Learning for Predicting Toxicity of Compounds Merely from Their Graphic Images. Journal of Chemical Information and Modeling. 58(8). 1533–1543. 93 indexed citations

13.

Dalal, Kush, Hélène Morin, Fuqiang Ban, et al.. (2018). Small molecule-induced degradation of the full length and V7 truncated variant forms of human androgen receptor. European Journal of Medicinal Chemistry. 157. 1164–1173. 14 indexed citations

14.

Lallous, Nada, Eric Leblanc, Fuqiang Ban, et al.. (2018). Computer-aided drug discovery of Myc-Max inhibitors as potential therapeutics for prostate cancer. European Journal of Medicinal Chemistry. 160. 108–119. 38 indexed citations

15.

Dalal, Kush, Meixia Che, Aishwariya Sharma, et al.. (2017). Bypassing Drug Resistance Mechanisms of Prostate Cancer with Small Molecules that Target Androgen Receptor–Chromatin Interactions. Molecular Cancer Therapeutics. 16(10). 2281–2291. 27 indexed citations

16.

Hassona, Mohamed D.H., Eric Leblanc, Kate Frewin, et al.. (2014). Identification of a Potent Antiandrogen that Targets the BF3 Site of the Androgen Receptor and Inhibits Enzalutamide-Resistant Prostate Cancer. Chemistry & Biology. 21(11). 1476–1485. 59 indexed citations

17.

Yu, Dehong, Fuqiang Ban, Mei Zhao, et al.. (2013). The use of nanoparticulate delivery systems in metronomic chemotherapy. Biomaterials. 34(16). 3925–3937. 19 indexed citations

18.

Bai, Fan, Chao Wang, Qin Lu, et al.. (2013). Nanoparticle-mediated drug delivery to tumor neovasculature to combat P-gp expressing multidrug resistant cancer. Biomaterials. 34(26). 6163–6174. 74 indexed citations

19.

Nandan, Devki, Martin Lopez, Fuqiang Ban, et al.. (2007). Indel‐based targeting of essential proteins in human pathogens that have close host orthologue(s): Discovery of selective inhibitors forLeishmania donovanielongation factor‐1α. Proteins Structure Function and Bioinformatics. 67(1). 53–64. 23 indexed citations

20.

Ban, Fuqiang, Maria Lundqvist, Russell J. Boyd, & Leif A. Eriksson. (2002). Theoretical Studies of the Cross-Linking Mechanisms between Cytosine and Tyrosine. Journal of the American Chemical Society. 124(11). 2753–2761. 32 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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