Hugo MacDermott-Opeskin

465 total citations
19 papers, 309 citations indexed

About

Hugo MacDermott-Opeskin is a scholar working on Molecular Biology, Materials Chemistry and Organic Chemistry. According to data from OpenAlex, Hugo MacDermott-Opeskin has authored 19 papers receiving a total of 309 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 5 papers in Materials Chemistry and 4 papers in Organic Chemistry. Recurrent topics in Hugo MacDermott-Opeskin's work include Lipid Membrane Structure and Behavior (6 papers), Protein Structure and Dynamics (4 papers) and Antibiotic Resistance in Bacteria (4 papers). Hugo MacDermott-Opeskin is often cited by papers focused on Lipid Membrane Structure and Behavior (6 papers), Protein Structure and Dynamics (4 papers) and Antibiotic Resistance in Bacteria (4 papers). Hugo MacDermott-Opeskin collaborates with scholars based in Australia, United States and United Kingdom. Hugo MacDermott-Opeskin's co-authors include Megan L. O’Mara, Katie A. Wilson, Christopher A. McDevitt, Michael G. Gardiner, Paul E. Kruger, Komal M. Patil, James A. Findlay, Dan Preston, Stephen J. Fairweather and Bart A. Eijkelkamp and has published in prestigious journals such as The Journal of Chemical Physics, Biochemistry and Biophysical Journal.

In The Last Decade

Hugo MacDermott-Opeskin

19 papers receiving 309 citations

Peers

Hugo MacDermott-Opeskin

SPECT

Karmen Čondić‐Jurkić Australia

Max Paoli United Kingdom

Ashok Nuthanakanti India

Kieran L. Hudson United Kingdom

Anna Mette Hansen Denmark

Jonathan Shearer United Kingdom

Hundeep Kaur Germany

Cammy K.-M. Chen Taiwan

Yun‐Ming Lin United States

Albert Canals Spain

Karmen Čondić‐Jurkić Australia

Hugo MacDermott-Opeskin

189 ×1.3

48 ×0.7

45 ×1.0

76 ×1.9

19 ×0.5

SPECT

Citations per year, relative to Hugo MacDermott-Opeskin Hugo MacDermott-Opeskin (= 1×) peers Karmen Čondić‐Jurkić

Countries citing papers authored by Hugo MacDermott-Opeskin

Since Specialization

Citations

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

Fields of papers citing papers by Hugo MacDermott-Opeskin

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hugo MacDermott-Opeskin. 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 Hugo MacDermott-Opeskin. The network helps show where Hugo MacDermott-Opeskin may publish in the future.

Co-authorship network of co-authors of Hugo MacDermott-Opeskin

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

All Works

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19 of 19 papers shown

Behara, Pavan Kumar, Michael M. Henry, Hugo MacDermott-Opeskin, et al.. (2024). Machine-learned molecular mechanics force fields from large-scale quantum chemical data. Chemical Science. 15(32). 12861–12878. 22 indexed citations

MacDermott-Opeskin, Hugo, Katie A. Wilson, Bart A. Eijkelkamp, & Megan L. O’Mara. (2023). Polyunsaturated lipids promote membrane phase separation and antimicrobial sensitivity. Biophysical Journal. 122(3). 322a–323a. 1 indexed citations

MacDermott-Opeskin, Hugo, et al.. (2023). SolvationAnalysis: A Python toolkit for understandingliquid solvation structure in classical molecular dynamicssimulations. The Journal of Open Source Software. 8(84). 5183–5183. 5 indexed citations

MacDermott-Opeskin, Hugo, Katie A. Wilson, & Megan L. O’Mara. (2023). The Impact of Antimicrobial Peptides on the Acinetobacter baumannii Inner Membrane Is Modulated by Lipid Polyunsaturation. ACS Infectious Diseases. 9(4). 815–826. 1 indexed citations

Barnoud, Jonathan, Oliver Beckstein, Richard Gowers, et al.. (2023). Building a community-driven ecosystem for fast, reproducible, and reusable molecular simulation analysis using mdanalysis. Biophysical Journal. 122(3). 420a–420a. 4 indexed citations

Andreas, Michael P., William Close, Reginald Young, et al.. (2022). Pore structure controls stability and molecular flux in engineered protein cages. Science Advances. 8(5). eabl7346–eabl7346. 40 indexed citations

Docker, Andrew, Hugo MacDermott-Opeskin, Heather M. Aitken, et al.. (2022). Hydroxy Groups Enhance [2]Rotaxane Anion Binding Selectivity. Chemistry - A European Journal. 28(28). e202200389–e202200389. 16 indexed citations

MacDermott-Opeskin, Hugo, et al.. (2022). Dynamics of the Acinetobacter baumannii inner membrane under exogenous polyunsaturated fatty acid stress. Biochimica et Biophysica Acta (BBA) - Biomembranes. 1864(7). 183908–183908. 9 indexed citations

Barnoud, Jonathan, Oliver Beckstein, Cédric Bouysset, et al.. (2022). MDAnalysis 2.0 and beyond: fast and interoperable, community driven simulation analysis. Biophysical Journal. 121(3). 272a–273a. 32 indexed citations

10.

MacDermott-Opeskin, Hugo, et al.. (2022). Lipid-mediated antimicrobial resistance: a phantom menace or a new hope?. Biophysical Reviews. 14(1). 145–162. 24 indexed citations

11.

MacDermott-Opeskin, Hugo, Xin Wu, Ariane Roseblade, et al.. (2022). Protonophoric and mitochondrial uncoupling activity of aryl-carbamate substituted fatty acids. Organic & Biomolecular Chemistry. 21(1). 132–139. 5 indexed citations

12.

Findlay, James A., Komal M. Patil, Michael G. Gardiner, et al.. (2022). Heteroleptic Tripalladium(II) Cages. Chemistry - An Asian Journal. 17(6). e202200093–e202200093. 34 indexed citations

13.

Wilson, Katie A., et al.. (2021). The role of plasmalogens, Forssman lipids, and sphingolipid hydroxylation in modulating the biophysical properties of the epithelial plasma membrane. The Journal of Chemical Physics. 154(9). 95101–95101. 12 indexed citations

14.

MacDermott-Opeskin, Hugo, Felise G. Adams, Varsha Naidu, et al.. (2021). The Membrane Composition Defines the Spatial Organization and Function of a Major Acinetobacter baumannii Drug Efflux System. mBio. 12(3). 19 indexed citations

15.

Neville, Stephanie L., Jennie Sjöhamn, Hugo MacDermott-Opeskin, et al.. (2021). The structural basis of bacterial manganese import. Science Advances. 7(32). 24 indexed citations

16.

Wilson, Katie A., et al.. (2020). Understanding the Link between Lipid Diversity and the Biophysical Properties of the Neuronal Plasma Membrane. Biochemistry. 59(33). 3010–3018. 19 indexed citations

17.

MacDermott-Opeskin, Hugo, Christopher A. McDevitt, & Megan L. O’Mara. (2020). Comparing Nonbonded Metal Ion Models in the Divalent Cation Binding Protein PsaA. Journal of Chemical Theory and Computation. 16(3). 1913–1923. 14 indexed citations

18.

Rawling, Tristan, Hugo MacDermott-Opeskin, Ariane Roseblade, et al.. (2020). Aryl urea substituted fatty acids: a new class of protonophoric mitochondrial uncoupler that utilises a synthetic anion transporter. Chemical Science. 11(47). 12677–12685. 22 indexed citations

19.

Wilson, Katie A., et al.. (2019). The Fats of Life: Using Computational Chemistry to Characterise the Eukaryotic Cell Membrane. Australian Journal of Chemistry. 73(3). 85–95. 6 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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