Khanh Tang

1.4k citations
6 papers · 756 indexed · 2 hit papers · h-index 4
Topics
Computational Drug Discovery Methods (4 papers)Protein Structure and Dynamics (2 papers)Microbial Natural Products and Biosynthesis (2 papers)
Partner nations
United StatesUkraine

In The Last Decade

Khanh Tang

6 papers receiving 740 citations

Hit Papers

ZINC20—A Free Ultralarge-Scale Chemical Database for Liga...202020262022202420202023100200300400500

Peers

Khanh Tang
Comparison fields: 5 of 102
  • Computational Theory and Mathematics 484
  • Molecular Biology 463
  • Materials Chemistry 177
  • Pharmacology 85
  • Organic Chemistry 84
Replace Mélaine A. Kuenemann with:
Mélaine A. Kuenemann France
Kateryna A. Tolmachova Switzerland
Tomohide Masuda Japan
Rishal Aggarwal India
Isha Singh United States
Jocelyn Sunseri United States
Stefano Rensi United States
Emilio Xavier Esposito United States
Paul Francoeur United States
Zunyun Fu China
Khanh Tang relative to Mélaine A. Kuenemann France Mélaine A. Kuenemann's profile →
Citations per field
00.5×3.3×
Mélaine A. Kuenemann · 1×
Citations per year

Countries citing papers authored by Khanh Tang

Since Specialization
Citations

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

Fields of papers citing papers by Khanh Tang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Khanh Tang

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

All Works

6 of 6 papers shown
#WorkIndexed citations
1 3
2 3
3
ZINC-22─A Free Multi-Billion-Scale Database of Tangible Compounds for Ligand Discoverybreakdown →
117
4 68
5
ZINC20—A Free Ultralarge-Scale Chemical Database for Ligand Discoverybreakdown →
556
6 9

About Khanh Tang

Khanh Tang is a scholar working on Computational Theory and Mathematics, Pharmacology and Ocean Engineering, having authored 6 papers that have together received 756 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), Protein Structure and Dynamics (2 papers) and Microbial Natural Products and Biosynthesis (2 papers). The work is most often cited by research in Computational Theory and Mathematics (484 citations), Molecular Biology (463 citations) and Pharmacology (85 citations). Khanh Tang has collaborated with scholars based in United States and Ukraine. Frequent co-authors include John J. Irwin, Yurii S. Moroz, Jennifer J. Young, John W. Mayfield, Roger A. Sayle, Brian K. Shoichet, Trent E. Balius, Yang Ying, Jiankun Lyu and Reed M. Stein. Their work appears in journals such as Tetrahedron Letters and Journal of Chemical Information and Modeling.

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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