Minyi Su
Impact in
- Computational Theory and Mathematics top 0.5%
- Computational Drug Discovery Methods
- Molecular Biology top 10%
- Protein Structure and Dynamics
- Bioinformatics and Genomic Networks
- vaccines and immunoinformatics approaches
- Chemical Synthesis and Analysis
Papers in
-
- Computational Drug Discovery Methods 9
-
- Protein Structure and Dynamics 5
- Chemical Synthesis and Analysis 2
- Protein purification and stability 1
- Co-authors
- Renxiao Wang (11 shared papers)Zhihai Liu (6 shared papers)Qifan Yang (3 shared papers)Guoqin Feng (2 shared papers)Yu Du (1 shared paper)Yan Li (1 shared paper)Li Han (2 shared papers)Jie Liu (1 shared paper)
In The Last Decade
Minyi Su
12 papers receiving 1.1k citations
Minyi Su's Hit Papers
Peers
Comparison fields: 5 of 89
- Computational Theory and Mathematics 919
- Molecular Biology 880
- Pharmacology 139
- Materials Chemistry 340
- Organic Chemistry 101
Countries citing papers authored by Minyi Su
This map shows the geographic impact of Minyi Su'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 Minyi Su with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Minyi Su more than expected).
Fields of papers citing papers by Minyi Su
This network shows the impact of papers produced by Minyi Su. 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 Minyi Su. The network helps show where Minyi Su may publish in the future.
Co-authors
The 25 scholars most cited alongside Minyi Su, 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 | Comparative Assessment of Scoring Functions: The CASF-2016 Update Hit paper breakdown → | 2018 | 552 |
| 2 | Forging the Basis for Developing Protein–Ligand Interaction Scoring Functions Hit paper breakdown → | 2017 | 346 |
| 3 | 2018 | 95 | |
| 4 | 2020 | 66 | |
| 5 | 2016 | 30 | |
| 6 | 2016 | 22 | |
| 7 | 2017 | 16 | |
| 8 | 2022 | 15 | |
| 9 | 2019 | 11 | |
| 10 | 2023 | 9 | |
| 11 | 2020 | 4 | |
| 12 | 2023 | 3 |
About Minyi Su
Minyi Su is a scholar working on Computational Theory and Mathematics, Molecular Biology, Materials Chemistry, Pharmacology and Spectroscopy, having authored 12 papers that have together received 1.2k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (9 papers), Protein Structure and Dynamics (5 papers), Analytical Chemistry and Chromatography (2 papers), Machine Learning in Materials Science (2 papers), Chemical Synthesis and Analysis (2 papers), Pharmacogenetics and Drug Metabolism (1 paper), Protein purification and stability (1 paper) and Enzyme Structure and Function (1 paper). The work is most often cited by research in Computational Theory and Mathematics (919 citations), Molecular Biology (880 citations), Pharmacology (139 citations), Materials Chemistry (340 citations) and Organic Chemistry (101 citations). Minyi Su has collaborated with scholars based in China and Macao. Frequent co-authors include Renxiao Wang, Zhihai Liu, Qifan Yang, Guoqin Feng, Yu Du, Yan Li, Li Han, Jie Liu, Yan Li and Yan Li. Their work appears in journals such as Journal of Chemical Information and Modeling, Bioinformatics, Nature Protocols, Accounts of Chemical Research and ACS Omega.
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.