Kaihsu Tai

1.3k total citations
28 papers, 1.1k citations indexed

About

Kaihsu Tai is a scholar working on Molecular Biology, Pharmacology and Computational Theory and Mathematics. According to data from OpenAlex, Kaihsu Tai has authored 28 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 7 papers in Pharmacology and 7 papers in Computational Theory and Mathematics. Recurrent topics in Kaihsu Tai's work include Protein Structure and Dynamics (7 papers), Computational Drug Discovery Methods (7 papers) and Cholinesterase and Neurodegenerative Diseases (7 papers). Kaihsu Tai is often cited by papers focused on Protein Structure and Dynamics (7 papers), Computational Drug Discovery Methods (7 papers) and Cholinesterase and Neurodegenerative Diseases (7 papers). Kaihsu Tai collaborates with scholars based in United Kingdom, United States and France. Kaihsu Tai's co-authors include Mark S.P. Sansom, J. Andrew McCammon, Tongye Shen, Oliver Beckstein, Richard H. Henchman, Marios Philippopoulos, Ulf Börjesson, Philip W. Fowler, Jennifer M. Bui and Andrew Hung and has published in prestigious journals such as Journal of the American Chemical Society, Journal of Biological Chemistry and Accounts of Chemical Research.

In The Last Decade

Kaihsu Tai

28 papers receiving 1.0k citations

Peers

Kaihsu Tai
Comparison fields: 5 of 101
  • Molecular Biology 666
  • Pharmacology 271
  • Computational Theory and Mathematics 249
  • Biomedical Engineering 144
  • Materials Chemistry 130
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Citations per field, relative to Kaihsu Tai
Kaihsu Tai · 1×
Citations per year, relative to Kaihsu Tai
Kaihsu Tai · 1×

Countries citing papers authored by Kaihsu Tai

Since Specialization
Citations

This map shows the geographic impact of Kaihsu Tai'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 Kaihsu Tai with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaihsu Tai more than expected).

Fields of papers citing papers by Kaihsu Tai

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kaihsu Tai. 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 Kaihsu Tai. The network helps show where Kaihsu Tai may publish in the future.

Co-authorship network of co-authors of Kaihsu Tai

This figure shows the co-authorship network connecting the top 25 collaborators of Kaihsu Tai. A scholar is included among the top collaborators of Kaihsu Tai 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 Kaihsu Tai. Kaihsu Tai is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 14
2 10
3 60
4 20
5 4
6 24
7
BioSimGRID: Application to analysis of membrane protein simulations
1
8 43
9 29
10
A Web / Grid Portal Implementation of BioSimGrid: A Biomolecular Simulation Database
2
11 80
12 37
13
Efficient data storage and analysis for generic biomolecular simulation data
2
14 39
15 36
16 49
17 108
18 26
19 136
20 8

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.

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