Shannon T. Smith

1.6k total citations · 1 hit paper
10 papers, 468 citations indexed

About

Shannon T. Smith is a scholar working on Molecular Biology, Computational Theory and Mathematics and Cancer Research. According to data from OpenAlex, Shannon T. Smith has authored 10 papers receiving a total of 468 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Molecular Biology, 3 papers in Computational Theory and Mathematics and 3 papers in Cancer Research. Recurrent topics in Shannon T. Smith's work include Protein Structure and Dynamics (3 papers), Computational Drug Discovery Methods (3 papers) and Blood Coagulation and Thrombosis Mechanisms (2 papers). Shannon T. Smith is often cited by papers focused on Protein Structure and Dynamics (3 papers), Computational Drug Discovery Methods (3 papers) and Blood Coagulation and Thrombosis Mechanisms (2 papers). Shannon T. Smith collaborates with scholars based in United States, Germany and France. Shannon T. Smith's co-authors include Allie Fu, Ling Geng, Marc O. Johnson, Melissa C. Skala, Ping Zhao, Robert J. Coffey, Michael L. Nickels, Jun Li, Jeffrey C. Rathmell and M. Schulte and has published in prestigious journals such as Nature Medicine, PLoS ONE and Biochemistry.

In The Last Decade

Shannon T. Smith

9 papers receiving 464 citations

Hit Papers

Pharmacological blockade of ASCT2-dependent glutamine tra... 2018 2026 2020 2023 2018 100 200 300

Peers

Shannon T. Smith
Laura C.A. Galbraith United Kingdom
Daniel S. Hitchcock United States
Tereza Golias Slovakia
Yeye Guo China
Shao Thing Teoh United States
L Petronio United States
Xiaofei Chen United States
Gopala K. Jarugumilli United States
Xin Lin Zu Australia
Robert L. Howell United States
Laura C.A. Galbraith United Kingdom
Shannon T. Smith
Citations per year, relative to Shannon T. Smith Shannon T. Smith (= 1×) peers Laura C.A. Galbraith

Countries citing papers authored by Shannon T. Smith

Since Specialization
Citations

This map shows the geographic impact of Shannon T. Smith'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 Shannon T. Smith with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shannon T. Smith more than expected).

Fields of papers citing papers by Shannon T. Smith

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Shannon T. Smith. 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 Shannon T. Smith. The network helps show where Shannon T. Smith may publish in the future.

Co-authorship network of co-authors of Shannon T. Smith

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

All Works

10 of 10 papers shown
1.
Miranda, Rafael de Souza, Shannon T. Smith, Mark Ultsch, et al.. (2025). Discovery and characterization of potent macrocycle inhibitors of ubiquitin-specific protease-7. Structure. 33(4). 705–717.e4.
2.
Schöler, Andrea, Shannon T. Smith, Jens Meiler, et al.. (2024). Groebke Blackburn Bienaymé-mediated multi-component synthesis of selective HDAC6 inhibitors with anti-inflammatory properties. Bioorganic Chemistry. 143. 107072–107072. 9 indexed citations
3.
Smith, Shannon T., Kevin Erreger, Brian J. Bender, et al.. (2024). Discovery of Protease-Activated Receptor 4 (PAR4)-Tethered Ligand Antagonists Using Ultralarge Virtual Screening. ACS Pharmacology & Translational Science. 7(4). 1086–1100. 7 indexed citations
4.
Smith, Shannon T., et al.. (2023). Rosetta’s Predictive Ability for Low-Affinity Ligand Binding in Fragment-Based Drug Discovery. Biochemistry. 62(3). 700–709. 3 indexed citations
5.
McDonald, Eli Fritz, Shannon T. Smith, Min‐Soo Kim, et al.. (2022). Structural Comparative Modeling of Multi-Domain F508del CFTR. Biomolecules. 12(3). 471–471. 11 indexed citations
6.
Smith, Shannon T., et al.. (2022). PlaceWaters: Real-time, explicit interface water sampling during Rosetta ligand docking. PLoS ONE. 17(5). e0269072–e0269072. 2 indexed citations
7.
Duvernay, Matthew T., Shannon T. Smith, Anna L. Blobaum, et al.. (2021). Discovery and Optimization of a Novel Series of Competitive and Central Nervous System-Penetrant Protease-Activated Receptor 4 (PAR4) Inhibitors. ACS Chemical Neuroscience. 12(24). 4524–4534. 3 indexed citations
8.
Smith, Shannon T. & Jens Meiler. (2020). Assessing multiple score functions in Rosetta for drug discovery. PLoS ONE. 15(10). e0240450–e0240450. 22 indexed citations
9.
Schulte, M., Allie Fu, Ping Zhao, et al.. (2018). Pharmacological blockade of ASCT2-dependent glutamine transport leads to antitumor efficacy in preclinical models. Nature Medicine. 24(2). 194–202. 379 indexed citations breakdown →
10.
DeMatteo, David, et al.. (2013). Investigating the role of the Psychopathy Checklist–Revised in United States case law.. Psychology Public Policy and Law. 20(1). 96–107. 32 indexed citations

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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