Peichen Pan
- Computational Theory and Mathematics top 0.5%
- Computational Drug Discovery Methods 43
- Molecular Biology top 5%
- Protein Structure and Dynamics 18
- Protein Kinase Regulation and GTPase Signaling 10
- Cancer therapeutics and mechanisms 8
- Ubiquitin and proteasome pathways 6
- Oncology top 10%
- Toxicology top 5%
- Pharmacology top 5%
- Microbial Natural Products and Biosynthesis 8
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- Machine Learning in Materials Science 9
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- Lung Cancer Treatments and Mutations 6
- Journals
- Journal of Medicinal Chemistry (10 papers)Journal of Chemical Information and Modeling (7 papers)Scientific Reports (6 papers)
- Partner nations
- ChinaMacaoUnited States
In The Last Decade
Peichen Pan
85 papers receiving 3.1k citations
Hit Papers
Peers
Comparison fields: 5 of 131
- Computational Theory and Mathematics 1.1k
- Molecular Biology 2.1k
- Oncology 376
- Toxicology 44
- Pharmacology 214
Countries citing papers authored by Peichen Pan
This map shows the geographic impact of Peichen Pan'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 Peichen Pan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peichen Pan more than expected).
Fields of papers citing papers by Peichen Pan
This network shows the impact of papers produced by Peichen Pan. 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 Peichen Pan. The network helps show where Peichen Pan may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Peichen Pan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 1 | |
| 2 | 2025 | 0 | |
| 3 | 2025 | 0 | |
| 4 | 2025 | 0 | |
| 5 | Discovery of antimicrobial peptides with notable antibacterial potency by an LLM-based foundation modelbreakdown → | 2025 | 33 |
| 6 | 2024 | 2 | |
| 7 | 2024 | 5 | |
| 8 | 2024 | 10 | |
| 9 | 2024 | 1 | |
| 10 | 2024 | 11 | |
| 11 | 2023 | 29 | |
| 12 | 2023 | 10 | |
| 13 | 2022 | 26 | |
| 14 | 2017 | 18 | |
| 15 | 2016 | 15 | |
| 16 | 2015 | 18 | |
| 17 | 2014 | 14 | |
| 18 | 2013 | 17 | |
| 19 | 2013 | 48 | |
| 20 | 2012 | 63 |
About Peichen Pan
Peichen Pan is a scholar working on Computational Theory and Mathematics, Molecular Biology and Pharmacology, having authored 88 papers that have together received 3.1k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (43 papers), Protein Structure and Dynamics (18 papers), Protein Kinase Regulation and GTPase Signaling (10 papers), Machine Learning in Materials Science (9 papers), Microbial Natural Products and Biosynthesis (8 papers), Cancer therapeutics and mechanisms (8 papers), Ubiquitin and proteasome pathways (6 papers) and Lung Cancer Treatments and Mutations (6 papers). The work is most often cited by research in Computational Theory and Mathematics (1.1k citations), Molecular Biology (2.1k citations) and Oncology (376 citations). Peichen Pan has collaborated with scholars based in China, Macao and United States. Frequent co-authors include Tingjun Hou, Youyong Li, Huiyong Sun, Dan Li, Sheng Tian, Lei Xu, Mingyun Shen, Fu Chen, Yan Guan and Yu Kang. Their work appears in journals such as Journal of Medicinal Chemistry, Journal of Chemical Information and Modeling, Scientific Reports, Journal of Cheminformatics and Physical Chemistry Chemical Physics.
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.