Chang‐Yu Hsieh
- Computational Theory and Mathematics top 0.1%
- Molecular Biology top 5%
- Materials Chemistry top 5%
- Artificial Intelligence top 2%
- Organic Chemistry top 5%
- Topics
- Computational Drug Discovery Methods (67 papers)Machine Learning in Materials Science (47 papers)Protein Structure and Dynamics (29 papers)
- Partner nations
- ChinaUnited StatesMacao
In The Last Decade
Chang‐Yu Hsieh
121 papers receiving 4.9k citations
Hit Papers
Peers
Comparison fields: 5 of 167
- Computational Theory and Mathematics 2.3k
- Molecular Biology 2.1k
- Materials Chemistry 1.2k
- Artificial Intelligence 678
- Organic Chemistry 574
Countries citing papers authored by Chang‐Yu Hsieh
This map shows the geographic impact of Chang‐Yu Hsieh'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 Chang‐Yu Hsieh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chang‐Yu Hsieh more than expected).
Fields of papers citing papers by Chang‐Yu Hsieh
This network shows the impact of papers produced by Chang‐Yu Hsieh. 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 Chang‐Yu Hsieh. The network helps show where Chang‐Yu Hsieh may publish in the future.
Co-authorship network of co-authors of Chang‐Yu Hsieh
This figure shows the co-authorship network connecting the top 25 collaborators of Chang‐Yu Hsieh. A scholar is included among the top collaborators of Chang‐Yu Hsieh 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 Chang‐Yu Hsieh. Chang‐Yu Hsieh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | 0 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | Discovery of antimicrobial peptides with notable antibacterial potency by an LLM-based foundation modelbreakdown → | 33 |
| 7 | 2 | |
| 8 | 5 | |
| 9 | 10 | |
| 10 | 2 | |
| 11 | 7 | |
| 12 | 28 | |
| 13 | 7 | |
| 14 | 8 | |
| 15 | 29 | |
| 16 | 10 | |
| 17 | 86 | |
| 18 | 34 | |
| 19 | ADMETlab 2.0: an integrated online platform for accurate and comprehensive predictions of ADMET propertiesbreakdown → | 1709 |
| 20 | 87 |
About Chang‐Yu Hsieh
Chang‐Yu Hsieh is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Molecular Biology, having authored 128 papers that have together received 5.0k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (67 papers), Machine Learning in Materials Science (47 papers) and Protein Structure and Dynamics (29 papers). The work is most often cited by research in Computational Theory and Mathematics (2.3k citations), Molecular Biology (2.1k citations) and Pharmacology (247 citations). Chang‐Yu Hsieh has collaborated with scholars based in China, United States and Macao. Frequent co-authors include Tingjun Hou, Dongsheng Cao, Zhenhua Wu, Chengkun Wu, Jiacai Yi, Zhijiang Yang, Li Fu, Aiping Lü, Xiangxiang Zeng and Guo‐Li Xiong. Their work appears in journals such as Chemical Reviews, Journal of the American Chemical Society and Physical Review 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.