Daniel S. Sem

2.3k total citations · 1 hit paper
86 papers, 1.8k citations indexed

About

Daniel S. Sem is a scholar working on Molecular Biology, Genetics and Computational Theory and Mathematics. According to data from OpenAlex, Daniel S. Sem has authored 86 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 58 papers in Molecular Biology, 17 papers in Genetics and 16 papers in Computational Theory and Mathematics. Recurrent topics in Daniel S. Sem's work include Computational Drug Discovery Methods (16 papers), Estrogen and related hormone effects (15 papers) and Protein Structure and Dynamics (13 papers). Daniel S. Sem is often cited by papers focused on Computational Drug Discovery Methods (16 papers), Estrogen and related hormone effects (15 papers) and Protein Structure and Dynamics (13 papers). Daniel S. Sem collaborates with scholars based in United States, Thailand and Zimbabwe. Daniel S. Sem's co-authors include Maurizio Pellecchia, Kurt Wüthrich, Charles B. Kasper, Phani Kumar Pullela, Taurai Chiku, Michael J. Carvan, John E. Thomas, R M Jack, Anna L. Shen and David Meininger and has published in prestigious journals such as Journal of the American Chemical Society, Nucleic Acids Research and Journal of Biological Chemistry.

In The Last Decade

Daniel S. Sem

86 papers receiving 1.8k citations

Hit Papers

Nmr in drug discovery 2002 2026 2010 2018 2002 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel S. Sem United States 24 1.1k 286 238 215 186 86 1.8k
Ivanov As Russia 22 944 0.9× 138 0.5× 259 1.1× 107 0.5× 209 1.1× 183 1.8k
Abhinav Nath United States 23 1.3k 1.2× 178 0.6× 153 0.6× 183 0.9× 234 1.3× 51 1.8k
Tove Sjögren Sweden 20 698 0.7× 175 0.6× 269 1.1× 180 0.8× 528 2.8× 28 1.8k
Laurence H. Patterson United Kingdom 28 1.2k 1.2× 177 0.6× 114 0.5× 145 0.7× 236 1.3× 89 2.2k
Nicole Bec France 26 1.2k 1.1× 153 0.5× 66 0.3× 110 0.5× 129 0.7× 71 2.3k
Robert X. Xu United States 22 1.5k 1.4× 168 0.6× 111 0.5× 247 1.1× 132 0.7× 30 2.3k
Gildas Bertho France 23 1.0k 1.0× 139 0.5× 89 0.4× 137 0.6× 140 0.8× 87 1.9k
Reetta Raag United States 15 965 0.9× 181 0.6× 193 0.8× 218 1.0× 629 3.4× 19 1.8k
Robert T. Gampe United States 22 2.0k 1.9× 364 1.3× 157 0.7× 210 1.0× 61 0.3× 49 2.9k
Luigi Di Costanzo Italy 30 1.6k 1.5× 383 1.3× 137 0.6× 601 2.8× 96 0.5× 75 2.9k

Countries citing papers authored by Daniel S. Sem

Since Specialization
Citations

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

Fields of papers citing papers by Daniel S. Sem

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel S. Sem. 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 Daniel S. Sem. The network helps show where Daniel S. Sem may publish in the future.

Co-authorship network of co-authors of Daniel S. Sem

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel S. Sem. A scholar is included among the top collaborators of Daniel S. Sem 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 Daniel S. Sem. Daniel S. Sem 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
1.
Frick, Karyn M., et al.. (2025). Not your mother's hormone therapy: Highly selective estrogen receptor beta agonists as next-generation therapies for menopausal symptom relief. Hormones and Behavior. 173. 105773–105773. 1 indexed citations
2.
Sweeney, Noreena L., Robert D. Bongard, Ankan Gupta, et al.. (2024). Structural and kinetic characterization of DUSP5 with a Di-phosphorylated tripeptide substrate from the ERK activation loop. SHILAP Revista de lepidopterología. 3. 3 indexed citations
3.
York, Jason, et al.. (2024). The novel estrogen receptor beta agonist EGX358 and APOE genotype influence memory, vasomotor, and anxiety outcomes in an Alzheimer’s mouse model. Frontiers in Aging Neuroscience. 16. 1477045–1477045. 3 indexed citations
4.
Sem, Daniel S., et al.. (2023). Real-time thiol detection in iPSC-derived neuron cultures using SemKur-IM, a novel fluorescent dithio probe. SLAS DISCOVERY. 29(3). 100127–100127. 1 indexed citations
5.
Lindeman, Sergey V., et al.. (2022). Discovery of two novel (4-hydroxyphenyl) substituted polycyclic carbocycles as potent and selective estrogen receptor beta agonists. Bioorganic & Medicinal Chemistry Letters. 73. 128906–128906. 1 indexed citations
6.
Sem, Daniel S.. (2014). Repurposing - Finding New Uses for Old (and Patented) Drugs: Bridging the "Valley of Death," to Translate Academic Research Into New Medicines. 18(1). 139. 1 indexed citations
7.
Gastonguay, Adam, Marat R. Talipov, Padmanabhan Vakeel, et al.. (2014). Protein expression, characterization and activity comparisons of wild type and mutant DUSP5 proteins. BMC Biochemistry. 15(1). 27–27. 13 indexed citations
8.
9.
Pullela, Phani Kumar, et al.. (2009). 13C-Methyl isocyanide as an NMR probe for cytochrome P450 active sites. Journal of Biomolecular NMR. 43(3). 171–178. 7 indexed citations
10.
Ge, Xia, Bassam T. Wakim, & Daniel S. Sem. (2008). Chemical Proteomics-Based Drug Design: Target and Antitarget Fishing with a Catechol−Rhodanine Privileged Scaffold for NAD(P)(H) Binding Proteins. Journal of Medicinal Chemistry. 51(15). 4571–4580. 31 indexed citations
11.
Yao, Huili, et al.. (2007). Structural evidence for a functionally relevant second camphor binding site in P450cam: Model for substrate entry into a P450 active site. Proteins Structure Function and Bioinformatics. 69(1). 125–138. 36 indexed citations
12.
Chiku, Taurai, Phani Kumar Pullela, & Daniel S. Sem. (2006). A Dithio-Coupled Kinase and ATPase Assay. SLAS DISCOVERY. 11(7). 844–853. 4 indexed citations
13.
Sem, Daniel S., et al.. (2004). Systems-Based Design of Bi-Ligand Inhibitors of OxidoreductasesFilling the Chemical Proteomic Toolbox. Chemistry & Biology. 11(2). 185–194. 13 indexed citations
14.
Pellecchia, Maurizio, et al.. (2002). NMR-based structural characterization of large protein-ligand interactions. Journal of Biomolecular NMR. 22(2). 165–173. 86 indexed citations
15.
Sem, Daniel S., et al.. (2001). Object-oriented approach to drug design enabled by NMR SOLVE: First real-time structural tool for characterizing protein-ligand interactions. Journal of Cellular Biochemistry. 84(S37). 99–105. 13 indexed citations
16.
Sem, Daniel S., et al.. (1999). Application of fluorescence polarization to the steady‐state enzyme kinetic analysis of calpain II. FEBS Letters. 443(1). 17–19. 13 indexed citations
17.
Sem, Daniel S., et al.. (1997). NMR Spectroscopic Studies of the DNA-binding Domain of the Monomer-binding Nuclear Orphan Receptor, Human Estrogen Related Receptor-2. Journal of Biological Chemistry. 272(29). 18038–18043. 26 indexed citations
19.
Sem, Daniel S. & Charles B. Kasper. (1994). Kinetic Mechanism for the Model Reaction of NADPH-Cytochrome P450 Oxidoreductase with Cytochrome c. Biochemistry. 33(40). 12012–12021. 22 indexed citations
20.
Sem, Daniel S. & W. W. Cleland. (1991). Phosphorylated aminosugars; synthesis, properties, and reactivity in enzymic reactions. Biochemistry. 30(20). 4978–4984. 21 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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