Fangping Wan

2.5k total citations · 2 hit papers
18 papers, 1.1k citations indexed

About

Fangping Wan is a scholar working on Molecular Biology, Computational Theory and Mathematics and Microbiology. According to data from OpenAlex, Fangping Wan has authored 18 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Molecular Biology, 7 papers in Computational Theory and Mathematics and 6 papers in Microbiology. Recurrent topics in Fangping Wan's work include Computational Drug Discovery Methods (6 papers), Antimicrobial Peptides and Activities (6 papers) and vaccines and immunoinformatics approaches (5 papers). Fangping Wan is often cited by papers focused on Computational Drug Discovery Methods (6 papers), Antimicrobial Peptides and Activities (6 papers) and vaccines and immunoinformatics approaches (5 papers). Fangping Wan collaborates with scholars based in United States, China and Belgium. Fangping Wan's co-authors include Jianyang Zeng, Tao Jiang, César de la Fuente‐Núñez, Dan Zhao, Lixiang Hong, Shuya Li, An Xiao, Hantao Shu, Marcelo D. T. Torres and James J. Collins and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and Bioinformatics.

In The Last Decade

Fangping Wan

17 papers receiving 1.1k citations

Hit Papers

Machine learning for antimicrobial peptide identification... 2024 2026 2025 2024 2024 25 50 75 100
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.

  1. Machine learning for antimicrobial peptide identification and design
    2024 102 citations Fangping Wan, Felix Wong et al. Nature Reviews Bioengineering profile →
  2. Deep-learning-enabled antibiotic discovery through molecular de-extinction
    2024 72 citations Fangping Wan, Marcelo D. T. Torres et al. Nature Biomedical Engineering profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Fangping Wan United States 13 847 523 162 161 87 18 1.1k
Yanyi Chu China 15 696 0.8× 383 0.7× 140 0.9× 44 0.3× 63 0.7× 20 912
Nalini Schaduangrat Thailand 22 1.4k 1.6× 420 0.8× 47 0.3× 334 2.1× 32 0.4× 63 1.8k
Christian Dallago Germany 14 2.0k 2.4× 337 0.6× 175 1.1× 100 0.6× 119 1.4× 22 2.3k
Alex T. Müller Switzerland 11 724 0.9× 411 0.8× 274 1.7× 329 2.0× 30 0.3× 19 994
Elif Özkırımlı Türkiye 16 459 0.5× 88 0.2× 83 0.5× 126 0.8× 84 1.0× 43 775
Tunca Doğan Türkiye 13 859 1.0× 615 1.2× 220 1.4× 17 0.1× 59 0.7× 28 1.2k
Wei‐Zhong Lin China 20 2.1k 2.5× 368 0.7× 34 0.2× 305 1.9× 85 1.0× 29 2.3k
Kuo‐Chen Chou United States 21 1.8k 2.1× 378 0.7× 72 0.4× 37 0.2× 18 0.2× 25 2.1k
Hai-Cheng Yi China 17 804 0.9× 362 0.7× 79 0.5× 55 0.3× 105 1.2× 39 1000

Countries citing papers authored by Fangping Wan

Since Specialization
Citations

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

Fields of papers citing papers by Fangping Wan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fangping Wan

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

All Works

18 of 18 papers shown
1.
Cesaro, Angela, Fangping Wan, C. Mark Maupin, et al.. (2025). Antiviral discovery using sparse datasets by integrating experiments, molecular simulations, and machine learning. Cell Reports Physical Science. 6(5). 102554–102554.
2.
Torres, Marcelo D. T., et al.. (2025). Generative latent diffusion language modeling yields anti-infective synthetic peptides. 1(9). 100183–100183. 1 indexed citations
3.
Wan, Fangping, Marcelo D. T. Torres, Changge Guan, & César de la Fuente‐Núñez. (2025). Tutorial: guidelines for the use of machine learning methods to mine genomes and proteomes for antibiotic discovery. Nature Protocols. 20(10). 2685–2697. 2 indexed citations
4.
Torres, Marcelo D. T., Fangping Wan, & César de la Fuente‐Núñez. (2025). Deep learning reveals antibiotics in the archaeal proteome. Nature Microbiology. 10(9). 2153–2167. 2 indexed citations
5.
Wan, Fangping, Marcelo D. T. Torres, Jacqueline Peng, & César de la Fuente‐Núñez. (2024). Deep-learning-enabled antibiotic discovery through molecular de-extinction. Nature Biomedical Engineering. 8(7). 854–871. 72 indexed citations breakdown →
6.
Wan, Fangping, Felix Wong, James J. Collins, & César de la Fuente‐Núñez. (2024). Machine learning for antimicrobial peptide identification and design. Nature Reviews Bioengineering. 2(5). 392–407. 102 indexed citations breakdown →
7.
Cesaro, Angela, Mojtaba Bagheri, Marcelo D. T. Torres, Fangping Wan, & César de la Fuente‐Núñez. (2023). Deep learning tools to accelerate antibiotic discovery. Expert Opinion on Drug Discovery. 18(11). 1245–1257. 32 indexed citations
8.
Li, Han, Xinyi Zhao, Shuya Li, et al.. (2022). Improving molecular property prediction through a task similarity enhanced transfer learning strategy. iScience. 25(10). 105231–105231. 17 indexed citations
9.
Wan, Fangping, et al.. (2022). Deep generative models for peptide design. Digital Discovery. 1(3). 195–208. 72 indexed citations
10.
Lei, Yipin, Shuya Li, Ziyi Liu, et al.. (2021). A deep-learning framework for multi-level peptide–protein interaction prediction. Nature Communications. 12(1). 5465–5465. 146 indexed citations
11.
Xiao, An, Fangping Wan, Shuya Li, et al.. (2021). A machine learning-based framework for modeling transcription elongation. Proceedings of the National Academy of Sciences. 118(6). 10 indexed citations
12.
Wan, Fangping, Shuya Li, Tingzhong Tian, et al.. (2020). EXP2SL: A Machine Learning Framework for Cell-Line-Specific Synthetic Lethality Prediction. Frontiers in Pharmacology. 11. 112–112. 19 indexed citations
13.
Hong, Lixiang, Shuya Li, Fangping Wan, et al.. (2020). A novel machine learning framework for automated biomedical relation extraction from large-scale literature repositories. Nature Machine Intelligence. 2(6). 347–355. 42 indexed citations
14.
Li, Yi, Chenxing Li, Fangping Wan, et al.. (2020). Secure multiparty computation for privacy-preserving drug discovery. Bioinformatics. 36(9). 2872–2880. 21 indexed citations
15.
Li, Shuya, Fangping Wan, Hantao Shu, et al.. (2020). MONN: A Multi-objective Neural Network for Predicting Compound-Protein Interactions and Affinities. Cell Systems. 10(4). 308–322.e11. 153 indexed citations
16.
Hu, Yan, Ziqiang Wang, Hailin Hu, et al.. (2019). ACME: pan-specific peptide–MHC class I binding prediction through attention-based deep neural networks. Bioinformatics. 35(23). 4946–4954. 74 indexed citations
17.
Wan, Fangping, Hailin Hu, Antao Dai, et al.. (2019). DeepCPI: A Deep Learning-Based Framework for Large-Scale in Silico Drug Screening. Genomics Proteomics & Bioinformatics. 17(5). 478–495. 64 indexed citations
18.
Wan, Fangping, Lixiang Hong, An Xiao, Tao Jiang, & Jianyang Zeng. (2018). NeoDTI: neural integration of neighbor information from a heterogeneous network for discovering new drug–target interactions. Bioinformatics. 35(1). 104–111. 256 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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