Lars Olsen

6.1k total citations · 1 hit paper
125 papers, 4.7k citations indexed

About

Lars Olsen is a scholar working on Molecular Biology, Pharmacology and Computational Theory and Mathematics. According to data from OpenAlex, Lars Olsen has authored 125 papers receiving a total of 4.7k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Molecular Biology, 36 papers in Pharmacology and 29 papers in Computational Theory and Mathematics. Recurrent topics in Lars Olsen's work include Pharmacogenetics and Drug Metabolism (36 papers), Computational Drug Discovery Methods (29 papers) and Geology and Paleoclimatology Research (21 papers). Lars Olsen is often cited by papers focused on Pharmacogenetics and Drug Metabolism (36 papers), Computational Drug Discovery Methods (29 papers) and Geology and Paleoclimatology Research (21 papers). Lars Olsen collaborates with scholars based in Denmark, Norway and Sweden. Lars Olsen's co-authors include Patrik Rydberg, Flemming Steen Jørgensen, Ulf Ryde, David E. Gloriam, Chris Oostenbrink, Lars Hemmingsen, Jed Zaretzki, Curt M. Breneman, Marco van de Weert and Minna Groenning and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of Biological Chemistry and Angewandte Chemie International Edition.

In The Last Decade

Lars Olsen

125 papers receiving 4.6k citations

Hit Papers

Deglaciation of Fennoscandia 2015 2026 2018 2022 2015 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Lars Olsen Denmark 39 1.9k 998 935 811 559 125 4.7k
Klaus R. Liedl Austria 51 4.1k 2.2× 876 0.9× 197 0.2× 714 0.9× 1.3k 2.3× 361 9.5k
John Caldwell United Kingdom 49 2.7k 1.4× 252 0.3× 1.5k 1.6× 943 1.2× 1.6k 2.8× 350 9.4k
Holger Fischer Switzerland 39 1.7k 0.9× 511 0.5× 243 0.3× 53 0.1× 367 0.7× 80 5.3k
Michel Rohmer France 61 8.6k 4.5× 143 0.1× 408 0.4× 1.2k 1.5× 275 0.5× 223 14.1k
Larry W. Robertson United States 53 2.2k 1.1× 185 0.2× 1.3k 1.4× 44 0.1× 737 1.3× 300 9.4k
Sandeep Kumar United States 45 4.8k 2.5× 442 0.4× 68 0.1× 568 0.7× 700 1.3× 129 9.2k
David E. Hibbs Australia 45 1.7k 0.9× 197 0.2× 124 0.1× 207 0.3× 378 0.7× 351 8.5k
Lev Weiner Israel 40 2.1k 1.1× 434 0.4× 153 0.2× 70 0.1× 179 0.3× 136 6.3k
Darío A. Estrı́n Argentina 46 3.6k 1.9× 142 0.1× 97 0.1× 156 0.2× 562 1.0× 238 7.1k
Toshimasa Ishida Japan 42 3.2k 1.7× 117 0.1× 112 0.1× 84 0.1× 872 1.6× 360 6.8k

Countries citing papers authored by Lars Olsen

Since Specialization
Citations

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

Fields of papers citing papers by Lars Olsen

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Lars Olsen

This figure shows the co-authorship network connecting the top 25 collaborators of Lars Olsen. A scholar is included among the top collaborators of Lars Olsen 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 Lars Olsen. Lars Olsen 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.
Hansen, Louise, et al.. (2023). Timing and maximum flood level of the Early Holocene glacial lake Nedre Glomsjø outburst flood, Norway. Boreas. 52(3). 295–313. 6 indexed citations
2.
Klug, Martin, et al.. (2021). Palaeolake sediment records reveal a mid‐ to late Younger Dryas ice‐sheet maximum in Mid‐Norway. Boreas. 51(1). 41–60. 9 indexed citations
3.
Wróbel, Tomasz M., Kasper Langebjerg Andersen, Rahul Yadav, et al.. (2020). Discovery of Novel Non-Steroidal Cytochrome P450 17A1 Inhibitors as Potential Prostate Cancer Agents. International Journal of Molecular Sciences. 21(14). 4868–4868. 8 indexed citations
4.
Tapken, Daniel, et al.. (2017). The low binding affinity of D-serine at the ionotropic glutamate receptor GluD2 can be attributed to the hinge region. Scientific Reports. 7(1). 46145–46145. 17 indexed citations
5.
Backe, Marie Balslev, Karl Bacos, Dan Ploug Christensen, et al.. (2017). Lysine demethylase inhibition protects pancreatic β cells from apoptosis and improves β-cell function. Molecular and Cellular Endocrinology. 460. 47–56. 19 indexed citations
6.
Montefiori, Marco, Stefan Kol, Mohammed Saddik Motawia, et al.. (2017). The CYP79A1 catalyzed conversion of tyrosine to (E)-p-hydroxyphenylacetaldoxime unravelled using an improved method for homology modeling. Phytochemistry. 135. 8–17. 8 indexed citations
7.
Olsen, Lars, Chris Oostenbrink, & Flemming Steen Jørgensen. (2015). Prediction of cytochrome P450 mediated metabolism. Advanced Drug Delivery Reviews. 86. 61–71. 76 indexed citations
9.
Rydberg, Patrik, Flemming Steen Jørgensen, & Lars Olsen. (2013). Use of density functional theory in drug metabolism studies. Expert Opinion on Drug Metabolism & Toxicology. 10(2). 215–227. 23 indexed citations
10.
Rydberg, Patrik & Lars Olsen. (2012). Predicting Drug Metabolism by Cytochrome P450 2C9: Comparison with the 2D6 and 3A4 Isoforms. ChemMedChem. 7(7). 1202–1209. 45 indexed citations
11.
Rydberg, Patrik, et al.. (2012). Nitrogen Inversion Barriers Affect the N‐Oxidation of Tertiary Alkylamines by Cytochromes P450. Angewandte Chemie International Edition. 52(3). 993–997. 22 indexed citations
12.
Zaretzki, Jed, Patrik Rydberg, Charles Bergeron, et al.. (2012). RS-Predictor Models Augmented with SMARTCyp Reactivities: Robust Metabolic Regioselectivity Predictions for Nine CYP Isozymes. Journal of Chemical Information and Modeling. 52(6). 1637–1659. 68 indexed citations
13.
Lohse, Brian, Jan B. L. Kristensen, Charlotte Helgstrand, et al.. (2011). Targeting Histone Lysine Demethylases by Truncating the Histone 3 Tail to Obtain Selective Substrate‐Based Inhibitors. Angewandte Chemie International Edition. 50(39). 9100–9103. 30 indexed citations
14.
Lohse, Brian, Jan B. L. Kristensen, Charlotte Helgstrand, et al.. (2011). Targeting Histone Lysine Demethylases by Truncating the Histone 3 Tail to Obtain Selective Substrate‐Based Inhibitors. Angewandte Chemie. 123(39). 9266–9269. 9 indexed citations
15.
Poongavanam, Vasanthanathan, Lars Olsen, Flemming Steen Jørgensen, Nico Vermeulen, & Chris Oostenbrink. (2010). Computational Prediction of Binding Affinity for CYP1A2-Ligand Complexes Using Empirical Free Energy Calculations. Drug Metabolism and Disposition. 38(8). 1347–1354. 30 indexed citations
16.
17.
Andersen, Jacob, Olivier Taboureau, Kasper B. Hansen, et al.. (2009). Location of the Antidepressant Binding Site in the Serotonin Transporter. Journal of Biological Chemistry. 284(15). 10276–10284. 93 indexed citations
18.
19.
Poongavanam, Vasanthanathan, Olivier Taboureau, Chris Oostenbrink, et al.. (2008). Classification of Cytochrome P450 1A2 Inhibitors and Noninhibitors by Machine Learning Techniques. Drug Metabolism and Disposition. 37(3). 658–664. 87 indexed citations
20.
Olsen, Lars, et al.. (2005). New leads of metallo-β-lactamase inhibitors from structure-based pharmacophore design. Bioorganic & Medicinal Chemistry. 14(8). 2627–2635. 43 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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