Teague Sterling

6.8k total citations · 3 hit papers
9 papers, 4.8k citations indexed

About

Teague Sterling is a scholar working on Molecular Biology, Computational Theory and Mathematics and Pharmacology. According to data from OpenAlex, Teague Sterling has authored 9 papers receiving a total of 4.8k indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Molecular Biology, 7 papers in Computational Theory and Mathematics and 2 papers in Pharmacology. Recurrent topics in Teague Sterling's work include Computational Drug Discovery Methods (7 papers), Protein Structure and Dynamics (3 papers) and Microbial Natural Products and Biosynthesis (2 papers). Teague Sterling is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Protein Structure and Dynamics (3 papers) and Microbial Natural Products and Biosynthesis (2 papers). Teague Sterling collaborates with scholars based in United States and Canada. Teague Sterling's co-authors include John J. Irwin, Ryan G. Coleman, Michael M. Mysinger, Erin S. D. Bolstad, Brian K. Shoichet, Allison K. Doak, Da Duan, Hayarpi Torosyan, Sarah Barelier and Matthew J. O’Meara and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and PLoS ONE.

In The Last Decade

Teague Sterling

9 papers receiving 4.7k citations

Hit Papers

ZINC 15 – Ligand Discovery for Everyone 2012 2026 2016 2021 2015 2012 2015 500 1000 1.5k 2.0k

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Teague Sterling United States 9 2.9k 2.5k 832 598 539 9 4.8k
Michael M. Mysinger United States 8 2.8k 1.0× 2.5k 1.0× 684 0.8× 558 0.9× 514 1.0× 8 4.4k
Niu Huang China 37 3.3k 1.2× 1.8k 0.7× 632 0.8× 808 1.4× 382 0.7× 130 5.4k
Jianfeng Pei China 34 3.2k 1.1× 2.4k 1.0× 752 0.9× 575 1.0× 482 0.9× 88 5.3k
Samuel Genheden Sweden 30 4.1k 1.4× 2.0k 0.8× 922 1.1× 899 1.5× 412 0.8× 70 6.2k
George Papadatos United Kingdom 19 3.0k 1.0× 3.0k 1.2× 793 1.0× 455 0.8× 624 1.2× 27 4.5k
Markus K. Dahlgren United States 12 2.7k 0.9× 1.3k 0.5× 564 0.7× 1.1k 1.8× 431 0.8× 17 4.9k
Jacob D. Durrant United States 33 2.9k 1.0× 1.7k 0.7× 666 0.8× 487 0.8× 336 0.6× 78 4.2k
A. Geoffrey Skillman United States 17 2.5k 0.9× 1.9k 0.8× 614 0.7× 693 1.2× 404 0.7× 26 3.8k
Jiyao Wang China 13 2.6k 0.9× 2.1k 0.8× 577 0.7× 459 0.8× 560 1.0× 57 5.2k
Maria A. Miteva France 37 3.2k 1.1× 2.0k 0.8× 442 0.5× 728 1.2× 541 1.0× 129 5.2k

Countries citing papers authored by Teague Sterling

Since Specialization
Citations

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

Fields of papers citing papers by Teague Sterling

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Teague Sterling

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

All Works

9 of 9 papers shown
1.
Estrada, Karol, Steven Froelich, Arthur Wüster, et al.. (2021). Identifying therapeutic drug targets using bidirectional effect genes. Nature Communications. 12(1). 2224–2224. 18 indexed citations
2.
Spence, Allyson L., Whitney E. Purtha, Janice Tam, et al.. (2018). Revealing the specificity of regulatory T cells in murine autoimmune diabetes. Proceedings of the National Academy of Sciences. 115(20). 5265–5270. 59 indexed citations
3.
Irwin, John J., et al.. (2017). Predicted Biological Activity of Purchasable Chemical Space. Journal of Chemical Information and Modeling. 58(1). 148–164. 37 indexed citations
4.
Irwin, John J., Da Duan, Hayarpi Torosyan, et al.. (2015). An Aggregation Advisor for Ligand Discovery. Journal of Medicinal Chemistry. 58(17). 7076–7087. 328 indexed citations breakdown →
5.
Barelier, Sarah, et al.. (2015). The Recognition of Identical Ligands by Unrelated Proteins. ACS Chemical Biology. 10(12). 2772–2784. 45 indexed citations
6.
Sterling, Teague & John J. Irwin. (2015). ZINC 15 – Ligand Discovery for Everyone. Journal of Chemical Information and Modeling. 55(11). 2324–2337. 2218 indexed citations breakdown →
7.
Coleman, Ryan G., Teague Sterling, & Dahlia R. Weiss. (2014). SAMPL4 & DOCK3.7: lessons for automated docking procedures. Journal of Computer-Aided Molecular Design. 28(3). 201–209. 17 indexed citations
8.
Coleman, Ryan G., et al.. (2013). Ligand Pose and Orientational Sampling in Molecular Docking. PLoS ONE. 8(10). e75992–e75992. 141 indexed citations
9.
Irwin, John J., Teague Sterling, Michael M. Mysinger, Erin S. D. Bolstad, & Ryan G. Coleman. (2012). ZINC: A Free Tool to Discover Chemistry for Biology. Journal of Chemical Information and Modeling. 52(7). 1757–1768. 1902 indexed citations breakdown →

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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