Brian K. Shoichet
- Computational Theory and Mathematics top 0.01%
- Computational Drug Discovery Methods 92
- Molecular Medicine top 0.05%
- Antibiotic Resistance in Bacteria 46
- Molecular Biology top 0.05%
- Protein Structure and Dynamics 44
- Receptor Mechanisms and Signaling 43
- Chemical Synthesis and Analysis 25
- Biochemical and Molecular Research 19
- Pharmacology top 0.05%
- Pharmacology top 0.05%
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- Enzyme Structure and Function 43
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- Click Chemistry and Applications 21
- Co-authors
- John J. IrwinIrwin D. KuntzSusan L. McGovernBryan L. RothMichael M. MysingerMichael J. KeiserBrian Y. FengNiu Huang
- Partner nations
- United StatesCanadaItaly
In The Last Decade
Brian K. Shoichet
255 papers receiving 35.5k citations
Hit Papers
Peers
Comparison fields: 5 of 191
- Computational Theory and Mathematics 15.6k
- Molecular Medicine 2.6k
- Molecular Biology 24.6k
- Pharmacology 4.6k
- Pharmacology 2.0k
Countries citing papers authored by Brian K. Shoichet
This map shows the geographic impact of Brian K. Shoichet'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 Brian K. Shoichet with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian K. Shoichet more than expected).
Fields of papers citing papers by Brian K. Shoichet
This network shows the impact of papers produced by Brian K. Shoichet. 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 Brian K. Shoichet. The network helps show where Brian K. Shoichet may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Brian K. Shoichet, 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 | 2025 | 12 | |
| 2 | 2025 | 0 | |
| 3 | 2024 | 19 | |
| 4 | 2024 | 7 | |
| 5 | 2024 | 18 | |
| 6 | 2023 | 10 | |
| 7 | 2023 | 19 | |
| 8 | 2022 | 18 | |
| 9 | 2021 | 23 | |
| 10 | 2021 | 135 | |
| 11 | 2020 | 63 | |
| 12 | 2020 | 41 | |
| 13 | 2018 | 54 | |
| 14 | 2018 | 30 | |
| 15 | 2017 | 21 | |
| 16 | 2015 | 60 | |
| 17 | 2012 | 134 | |
| 18 | 2011 | 104 | |
| 19 | 2010 | 190 | |
| 20 | 2007 | 59 |
About Brian K. Shoichet
Brian K. Shoichet is a scholar working on Molecular Medicine, Computational Theory and Mathematics and Molecular Biology, having authored 260 papers that have together received 36.4k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (92 papers), Antibiotic Resistance in Bacteria (46 papers), Protein Structure and Dynamics (44 papers), Enzyme Structure and Function (43 papers), Receptor Mechanisms and Signaling (43 papers), Chemical Synthesis and Analysis (25 papers), Click Chemistry and Applications (21 papers) and Biochemical and Molecular Research (19 papers). The work is most often cited by research in Computational Theory and Mathematics (15.6k citations), Molecular Medicine (2.6k citations) and Molecular Biology (24.6k citations). Brian K. Shoichet has collaborated with scholars based in United States, Canada and Italy. Frequent co-authors include John J. Irwin, Irwin D. Kuntz, Susan L. McGovern, Bryan L. Roth, Michael M. Mysinger, Michael J. Keiser, Brian Y. Feng, Niu Huang, Elaine C. Meng and Emilia Caselli. Their work appears in journals such as Nature, Science and Cell.
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.