Ziduo Yang
Impact in
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- Computational Drug Discovery Methods
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- Protein Structure and Dynamics
- Bioinformatics and Genomic Networks
- Machine Learning in Bioinformatics
- Metabolomics and Mass Spectrometry Studies
Papers in
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- Computational Drug Discovery Methods 14
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- Machine Learning in Materials Science 13
- Co-authors
- Calvin Yu‐Chian Chen (17 shared papers)Weihe Zhong (10 shared papers)Lu Zhao (6 shared papers)Qiujie Lv (12 shared papers)Guanxing Chen (4 shared papers)Lei Shen (6 shared papers)Zhaoshan Liu (2 shared papers)Chau Hung Lee (2 shared papers)
In The Last Decade
Ziduo Yang
25 papers receiving 906 citations
Ziduo Yang's Hit Papers
Peers
Comparison fields: 5 of 90
- Computational Theory and Mathematics 528
- Molecular Biology 443
- Health Informatics 7
- Materials Chemistry 244
- Radiology, Nuclear Medicine and Imaging 76
Countries citing papers authored by Ziduo Yang
This map shows the geographic impact of Ziduo Yang'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 Ziduo Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ziduo Yang more than expected).
Fields of papers citing papers by Ziduo Yang
This network shows the impact of papers produced by Ziduo Yang. 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 Ziduo Yang. The network helps show where Ziduo Yang may publish in the future.
Co-authors
The 25 scholars most cited alongside Ziduo Yang, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 26 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction Hit paper breakdown → | 2022 | 226 |
| 2 | 2022 | 83 | |
| 3 | 2023 | 75 | |
| 4 | 2021 | 73 | |
| 5 | 2023 | 69 | |
| 6 | 2023 | 66 | |
| 7 | 2023 | 50 | |
| 8 | 2021 | 43 | |
| 9 | 2023 | 34 | |
| 10 | 2023 | 31 | |
| 11 | 2024 | 24 | |
| 12 | 2023 | 22 | |
| 13 | 2021 | 19 | |
| 14 | 2024 | 19 | |
| 15 | 2024 | 18 | |
| 16 | 2025 | 17 | |
| 17 | 2021 | 15 | |
| 18 | 2021 | 8 | |
| 19 | 2022 | 6 | |
| 20 | 2025 | 4 |
About Ziduo Yang
Ziduo Yang is a scholar working on Computational Theory and Mathematics, Materials Chemistry, Molecular Biology, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging, having authored 26 papers that have together received 913 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (14 papers), Machine Learning in Materials Science (13 papers), Protein Structure and Dynamics (4 papers), Metabolomics and Mass Spectrometry Studies (3 papers), COVID-19 diagnosis using AI (3 papers), Bioinformatics and Genomic Networks (3 papers), AI in cancer detection (3 papers) and Advanced Neural Network Applications (2 papers). The work is most often cited by research in Computational Theory and Mathematics (528 citations), Molecular Biology (443 citations), Health Informatics (7 citations), Materials Chemistry (244 citations) and Radiology, Nuclear Medicine and Imaging (76 citations). Ziduo Yang has collaborated with scholars based in China, Taiwan and Singapore. Frequent co-authors include Calvin Yu‐Chian Chen, Weihe Zhong, Lu Zhao, Qiujie Lv, Guanxing Chen, Lei Shen, Zhaoshan Liu, Chau Hung Lee, Shuyu Wu and Yifan Li. Their work appears in journals such as Chemical Science, npj Computational Materials, Nature Communications, Medical Physics and The Journal of Physical Chemistry Letters.
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.