Xiaozhe Wan
Impact in
- Computational Theory and Mathematics top 0.5%
- Computational Drug Discovery Methods
- Materials Chemistry top 10%
- Machine Learning in Materials Science
Papers in
-
- Protein Structure and Dynamics 4
- Bioinformatics and Genomic Networks 3
- Genetics, Bioinformatics, and Biomedical Research 2
- Machine Learning in Bioinformatics 1
- Protein Degradation and Inhibitors 1
-
- Computational Drug Discovery Methods 8
- Co-authors
- Mingyue Zheng (12 shared papers)Hualiang Jiang (9 shared papers)Xutong Li (9 shared papers)Xiaohong Liu (7 shared papers)Kaixian Chen (7 shared papers)Dingyan Wang (5 shared papers)Feisheng Zhong (6 shared papers)Xiaomin Luo (5 shared papers)
- Journals
- Journal of Medicinal Chemistry (3 papers)Organic Chemistry Frontiers (1 paper)Chemical Science (1 paper)Protein & Cell (1 paper)Bioinformatics (1 paper)
- Partner nations
- ChinaMacaoUnited States
In The Last Decade
Xiaozhe Wan
13 papers receiving 1.2k citations
Xiaozhe Wan's Hit Papers
Peers
Comparison fields: 5 of 113
- Computational Theory and Mathematics 681
- Materials Chemistry 458
- Oncology 234
- Molecular Biology 575
- Pharmacology 39
Countries citing papers authored by Xiaozhe Wan
This map shows the geographic impact of Xiaozhe 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 Xiaozhe Wan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaozhe Wan more than expected).
Fields of papers citing papers by Xiaozhe Wan
This network shows the impact of papers produced by Xiaozhe 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 Xiaozhe Wan. The network helps show where Xiaozhe Wan may publish in the future.
Co-authors
The 25 scholars most cited alongside Xiaozhe Wan, 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 | Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism Hit paper breakdown → | 2019 | 645 |
| 2 | 2018 | 263 | |
| 3 | 2019 | 62 | |
| 4 | 2021 | 51 | |
| 5 | 2019 | 36 | |
| 6 | 2020 | 35 | |
| 7 | 2017 | 29 | |
| 8 | 2021 | 29 | |
| 9 | 2022 | 17 | |
| 10 | 2024 | 16 | |
| 11 | 2019 | 6 | |
| 12 | 2024 | 4 | |
| 13 | 2024 | 1 | |
| 14 | 2020 | 1 |
About Xiaozhe Wan
Xiaozhe Wan is a scholar working on Molecular Biology, Computational Theory and Mathematics, Materials Chemistry, Oncology and Organic Chemistry, having authored 14 papers that have together received 1.2k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (8 papers), Protein Structure and Dynamics (4 papers), Machine Learning in Materials Science (3 papers), Bioinformatics and Genomic Networks (3 papers), Genetics, Bioinformatics, and Biomedical Research (2 papers), Machine Learning in Bioinformatics (1 paper), Protein Degradation and Inhibitors (1 paper) and Microbial Natural Products and Biosynthesis (1 paper). The work is most often cited by research in Computational Theory and Mathematics (681 citations), Materials Chemistry (458 citations), Oncology (234 citations), Molecular Biology (575 citations) and Pharmacology (39 citations). Xiaozhe Wan has collaborated with scholars based in China, Macao and United States. Frequent co-authors include Mingyue Zheng, Hualiang Jiang, Xutong Li, Xiaohong Liu, Kaixian Chen, Dingyan Wang, Feisheng Zhong, Xiaomin Luo, Zhaoping Xiong and Zhaojun Li. Their work appears in journals such as Journal of Medicinal Chemistry, Organic Chemistry Frontiers, Chemical Science, Protein & Cell and Bioinformatics.
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.