Pan Tan

558 total citations
27 papers, 312 citations indexed

About

Pan Tan is a scholar working on Molecular Biology, Materials Chemistry and Atomic and Molecular Physics, and Optics. According to data from OpenAlex, Pan Tan has authored 27 papers receiving a total of 312 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 9 papers in Materials Chemistry and 6 papers in Atomic and Molecular Physics, and Optics. Recurrent topics in Pan Tan's work include Protein Structure and Dynamics (11 papers), RNA and protein synthesis mechanisms (6 papers) and Machine Learning in Bioinformatics (5 papers). Pan Tan is often cited by papers focused on Protein Structure and Dynamics (11 papers), RNA and protein synthesis mechanisms (6 papers) and Machine Learning in Bioinformatics (5 papers). Pan Tan collaborates with scholars based in China, United States and United Kingdom. Pan Tan's co-authors include Liang Hong, Eugene Mamontov, Xiangjun Xing, Jinglai Li, Qin Xu, Lirong Zheng, Jeremy C. Smith, Loukas Petridis, Zhuo Liu and Guisheng Fan and has published in prestigious journals such as Physical Review Letters, Nature Communications and The Journal of Chemical Physics.

In The Last Decade

Pan Tan

25 papers receiving 300 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pan Tan China	11	154	73	60	40	36	27	312
Saumyak Mukherjee India	13	272 1.8×	95 1.3×	132 2.2×	33 0.8×	51 1.4×	23	454
Tetsuro Nagai Japan	10	170 1.1×	65 0.9×	79 1.3×	50 1.3×	23 0.6×	28	304
Roberto A. Garza‐López United States	6	158 1.0×	38 0.5×	57 0.9×	138 3.5×	17 0.5×	29	342
Giovanni De Matteis Italy	11	76 0.5×	147 2.0×	63 1.1×	26 0.7×	58 1.6×	29	426
Patrice Delarue France	14	287 1.9×	139 1.9×	105 1.8×	67 1.7×	26 0.7×	33	496
Stefan Kesselheim Germany	9	65 0.4×	67 0.9×	25 0.4×	98 2.5×	28 0.8×	18	233
Changsun Eun United States	13	281 1.8×	124 1.7×	110 1.8×	114 2.9×	33 0.9×	29	515
Ahmet Yıldırım Türkiye	9	165 1.1×	154 2.1×	106 1.8×	57 1.4×	37 1.0×	19	421
Chuanqi Li China	11	169 1.1×	113 1.5×	78 1.3×	144 3.6×	25 0.7×	24	406
Hiqmet Kamberaj United States	11	231 1.5×	113 1.5×	95 1.6×	62 1.6×	72 2.0×	32	488

Countries citing papers authored by Pan Tan

Since Specialization

Citations

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

Fields of papers citing papers by Pan Tan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pan Tan

This figure shows the co-authorship network connecting the top 25 collaborators of Pan Tan. A scholar is included among the top collaborators of Pan Tan 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 Pan Tan. Pan Tan 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

Zheng, Lirong, et al.. (2025). Harnessing protein language model for structure-based discovery of highly efficient and robust PET hydrolases. Nature Communications. 16(1). 6211–6211. 5 indexed citations

Tan, Pan, et al.. (2025). Harnessing deep learning to accelerate the development of antibodies and aptamers. Acta Pharmaceutica Sinica B. 16(2). 788–801.

Zhang, Liang, Hua Pang, Chenghao Zhang, et al.. (2025). VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data. Acta Pharmaceutica Sinica B. 15(5). 2454–2467. 2 indexed citations

Zhang, Liang, et al.. (2024). Enhancing efficiency of protein language models with minimal wet-lab data through few-shot learning. Nature Communications. 15(1). 5566–5566. 25 indexed citations

Tan, Pan, Yun Chan Kang, Yi Zong, et al.. (2024). AI-enabled alkaline-resistant evolution of protein to apply in mass production. eLife. 13. 5 indexed citations

Li, Mingchen, et al.. (2024). PETA: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications. Journal of Cheminformatics. 16(1). 92–92. 7 indexed citations

Tan, Pan, et al.. (2024). Optimizing enzyme thermostability by combining multiple mutations using protein language model. SHILAP Revista de lepidopterología. 3(4). 492–504. 13 indexed citations

Zheng, Lirong, et al.. (2024). Simple, Efficient, and Scalable Structure-Aware Adapter Boosts Protein Language Models. Journal of Chemical Information and Modeling. 64(16). 6338–6349. 10 indexed citations

Suo, Liumin, et al.. (2023). The evolution of anionic nanoclusters at the electrode interface in water-in-salt electrolytes. Physical Chemistry Chemical Physics. 25(15). 10301–10312. 2 indexed citations

10.

Li, Mingchen, Yi Xiong, Yu Guang Wang, et al.. (2023). SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering. Journal of Cheminformatics. 15(1). 12–12. 22 indexed citations

11.

Zhao, Weishu, Lirong Zheng, Pan Tan, et al.. (2022). Proteome-wide 3D structure prediction provides insights into the ancestral metabolism of ancient archaea and bacteria. Nature Communications. 13(1). 7861–7861. 12 indexed citations

12.

Zheng, Lirong, Zhuo Liu, Song Li, et al.. (2022). Universal dynamical onset in water at distinct material interfaces. Chemical Science. 13(15). 4341–4351. 12 indexed citations

13.

Tan, Pan, et al.. (2022). A random batch Ewald method for charged particles in the isothermal–isobaric ensemble. The Journal of Chemical Physics. 157(14). 144102–144102. 13 indexed citations

14.

Tan, Pan, Jinming Yue, Binghang Liu, et al.. (2021). Solid-Like Nano-Anion Cluster Constructs a Free Lithium-Ion-Conducting Superfluid Framework in a Water-in-Salt Electrolyte. The Journal of Physical Chemistry C. 125(22). 11838–11847. 32 indexed citations

15.

Tan, Pan, Juan Huang, Eugene Mamontov, et al.. (2020). Decoupling between the translation and rotation of water in the proximity of a protein molecule. Physical Chemistry Chemical Physics. 22(32). 18132–18140. 17 indexed citations

16.

Tan, Pan, et al.. (2020). Differences between calcium rich and depleted alpha-lactalbumin investigated by molecular dynamics simulations and incoherent neutron scattering. Physical review. E. 101(3). 32415–32415. 4 indexed citations

17.

Yu, Minhao, et al.. (2020). One-dimensional nature of protein low-energy vibrations. Physical Review Research. 2(3). 9 indexed citations

18.

Liu, Liqiong, Susu Jiang, Chao Chen, et al.. (2020). Structural Analysis of an l -Cysteine Desulfurase from an Ssp DNA Phosphorothioation System. mBio. 11(2). 10 indexed citations

19.

Ma, Xiaodan, Qianqian Wang, Yuwen Li, et al.. (2019). How BamA recruits OMP substrates via poly‐POTRAs domain. The FASEB Journal. 33(12). 14690–14702. 5 indexed citations

20.

Tan, Pan, Qin Xu, Eugene Mamontov, et al.. (2018). Gradual Crossover from Subdiffusion to Normal Diffusion: A Many-Body Effect in Protein Surface Water. Physical Review Letters. 120(24). 248101–248101. 61 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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