Mark Mackey

1.4k total citations
19 papers, 980 citations indexed

About

Mark Mackey is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, Mark Mackey has authored 19 papers receiving a total of 980 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 13 papers in Computational Theory and Mathematics and 5 papers in Materials Chemistry. Recurrent topics in Mark Mackey's work include Computational Drug Discovery Methods (13 papers), Protein Structure and Dynamics (7 papers) and Chemical Synthesis and Analysis (3 papers). Mark Mackey is often cited by papers focused on Computational Drug Discovery Methods (13 papers), Protein Structure and Dynamics (7 papers) and Chemical Synthesis and Analysis (3 papers). Mark Mackey collaborates with scholars based in United Kingdom, United States and Italy. Mark Mackey's co-authors include Andy Vinter, Sally Rose, Matthias R. Bauer, James L. Melville, Julien Michel, Paolo Tosco, Antonia S. J. S. Mey, Jeremy G. Vinter, Maximilian Kühn and Stuart Firth‐Clark and has published in prestigious journals such as Journal of the American Chemical Society, Journal of Medicinal Chemistry and Physical Chemistry Chemical Physics.

In The Last Decade

Mark Mackey

18 papers receiving 940 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mark Mackey United Kingdom 11 590 439 190 168 103 19 980
Trung Hai Nguyen Vietnam 18 552 0.9× 382 0.9× 132 0.7× 181 1.1× 93 0.9× 54 1.2k
Florian Flachsenberg Germany 12 662 1.1× 541 1.2× 293 1.5× 112 0.7× 111 1.1× 20 1.1k
John Marelius Sweden 10 828 1.4× 382 0.9× 193 1.0× 161 1.0× 78 0.8× 11 1.1k
Alfonso T. García‐Sosa Estonia 22 675 1.1× 490 1.1× 127 0.7× 287 1.7× 78 0.8× 54 1.2k
Katrina W. Lexa United States 20 859 1.5× 434 1.0× 192 1.0× 213 1.3× 92 0.9× 27 1.3k
Delaram Ghoreishi United States 5 616 1.0× 246 0.6× 157 0.8× 235 1.4× 110 1.1× 5 1.2k
Kenneth Borrelli United States 17 898 1.5× 373 0.8× 147 0.8× 247 1.5× 84 0.8× 17 1.3k
Mark McGann United States 7 893 1.5× 600 1.4× 157 0.8× 303 1.8× 144 1.4× 11 1.4k
Kunqian Yu China 23 844 1.4× 326 0.7× 197 1.0× 211 1.3× 95 0.9× 56 1.4k
Changge Ji China 17 630 1.1× 308 0.7× 268 1.4× 144 0.9× 57 0.6× 34 1.1k

Countries citing papers authored by Mark Mackey

Since Specialization
Citations

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

Fields of papers citing papers by Mark Mackey

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Mark Mackey

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

All Works

19 of 19 papers shown
1.
Habgood, Matthew, et al.. (2025). Adaptive Lambda Scheduling: A Method for Computational Efficiency in Free Energy Perturbation Simulations. Journal of Chemical Information and Modeling. 65(2). 512–516. 4 indexed citations
2.
Ramaswamy, Venkata Krishnan, Matthew Habgood, & Mark Mackey. (2025). Active Learning FEP Using 3D-QSAR for Prioritizing Bioisosteres in Medicinal Chemistry. ACS Medicinal Chemistry Letters. 16(6). 984–990.
3.
Mackey, Mark, et al.. (2023). Discovery of High-Affinity Amyloid Ligands Using a Ligand-Based Virtual Screening Pipeline. Journal of the American Chemical Society. 145(29). 15936–15950. 24 indexed citations
4.
Horton, Joshua T., Simon Boothroyd, Jeffrey Wagner, et al.. (2022). Open Force Field BespokeFit: Automating Bespoke Torsion Parametrization at Scale. Journal of Chemical Information and Modeling. 62(22). 5622–5633. 35 indexed citations
5.
Mackey, Mark, et al.. (2022). Data-driven generation of perturbation networks for relative binding free energy calculations. Digital Discovery. 1(6). 870–885. 6 indexed citations
6.
Kühn, Maximilian, Stuart Firth‐Clark, Paolo Tosco, et al.. (2020). Assessment of Binding Affinity via Alchemical Free-Energy Calculations. Journal of Chemical Information and Modeling. 60(6). 3120–3130. 149 indexed citations
7.
Mey, Antonia S. J. S., et al.. (2020). Hybrid Alchemical Free Energy/Machine-Learning Methodology for the Computation of Hydration Free Energies. Journal of Chemical Information and Modeling. 60(11). 5331–5339. 38 indexed citations
8.
Bauer, Matthias R. & Mark Mackey. (2019). Electrostatic Complementarity as a Fast and Effective Tool to Optimize Binding and Selectivity of Protein–Ligand Complexes. Journal of Medicinal Chemistry. 62(6). 3036–3050. 122 indexed citations
9.
Mackey, Mark, et al.. (2014). Porting a commercial application to OpenCL. 1–10. 3 indexed citations
10.
Farwer, Jochen, et al.. (2013). Footprinting molecular electrostatic potential surfaces for calculation of solvation energies. Physical Chemistry Chemical Physics. 15(41). 18262–18262. 45 indexed citations
11.
Mackey, Mark, et al.. (2011). High Content Pharmacophores from Molecular Fields: A Biologically Relevant Method for Comparing and Understanding Ligands. Current Computer - Aided Drug Design. 7(3). 190–205. 28 indexed citations
12.
Mackey, Mark & James L. Melville. (2009). Better than Random? The Chemotype Enrichment Problem. Journal of Chemical Information and Modeling. 49(5). 1154–1162. 48 indexed citations
13.
Mackey, Mark, et al.. (2008). FieldScreen: Virtual Screening Using Molecular Fields. Application to the DUD Data Set. Journal of Chemical Information and Modeling. 48(11). 2108–2117. 95 indexed citations
14.
Mackey, Mark, et al.. (2008). Rapid discovery of new leads for difficult targets: application to CCK2 and 11beta-HSD1. Chemistry Central Journal. 2(S1). 1 indexed citations
15.
Mackey, Mark, et al.. (2007). Molecular field technology applied to virtual screening and finding the bioactive conformation. Expert Opinion on Drug Discovery. 2(1). 131–144. 59 indexed citations
16.
Mackey, Mark, et al.. (2006). Molecular Field Extrema as Descriptors of Biological Activity:  Definition and Validation. Journal of Chemical Information and Modeling. 46(2). 665–676. 311 indexed citations
17.
Mackey, Mark, et al.. (2004). Peptides to non-peptides: leads from structureless virtual screening. 2(2). 57–60. 5 indexed citations
18.
Hao, Ming, et al.. (2002). On-the-fly visualization and debugging of parallel programs. 634. 386–391. 5 indexed citations
19.
Baiardi, Fabrizio, Marco Danelutto, Mehdi Jazayeri, et al.. (2002). Pisa parallel processing project on general-purpose highly-parallel computers. CINECA IRIS Institutial research information system (University of Pisa). 536–543. 2 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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