Mike J. Bodkin

580 total citations
15 papers, 420 citations indexed

About

Mike J. Bodkin is a scholar working on Molecular Biology, Cellular and Molecular Neuroscience and Computational Theory and Mathematics. According to data from OpenAlex, Mike J. Bodkin has authored 15 papers receiving a total of 420 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 8 papers in Cellular and Molecular Neuroscience and 8 papers in Computational Theory and Mathematics. Recurrent topics in Mike J. Bodkin's work include Receptor Mechanisms and Signaling (10 papers), Computational Drug Discovery Methods (8 papers) and Neuropeptides and Animal Physiology (8 papers). Mike J. Bodkin is often cited by papers focused on Receptor Mechanisms and Signaling (10 papers), Computational Drug Discovery Methods (8 papers) and Neuropeptides and Animal Physiology (8 papers). Mike J. Bodkin collaborates with scholars based in United Kingdom, Japan and United States. Mike J. Bodkin's co-authors include Alexander Heifetz, Philip C. Biggin, Matteo Aldeghi, Dmitri G. Fedorov, Ewa I. Chudyk, Michelle Southey, Andrea Townsend‐Nicholson, Iñaki Morao, Vadim Cherezov and Rocco Meli and has published in prestigious journals such as Journal of Medicinal Chemistry, Journal of Computational Chemistry and Journal of Chemical Theory and Computation.

In The Last Decade

Mike J. Bodkin

14 papers receiving 418 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Mike J. Bodkin United Kingdom 11 328 207 78 60 59 15 420
Apurba Bhattarai United States 11 462 1.4× 157 0.8× 80 1.0× 62 1.0× 73 1.2× 16 590
Sara E. Nichols United States 11 483 1.5× 202 1.0× 54 0.7× 115 1.9× 66 1.1× 14 603
Willem Jespers Netherlands 15 434 1.3× 138 0.7× 64 0.8× 92 1.5× 40 0.7× 44 615
Andrea C. McReynolds United States 8 407 1.2× 142 0.7× 95 1.2× 26 0.4× 36 0.6× 9 564
Jagna Witek Poland 11 385 1.2× 171 0.8× 72 0.9× 35 0.6× 43 0.7× 14 530
Sirish Kaushik Lakkaraju United States 15 602 1.8× 313 1.5× 93 1.2× 62 1.0× 63 1.1× 24 751
Petr Popov Russia 14 468 1.4× 214 1.0× 98 1.3× 107 1.8× 48 0.8× 39 604
Manuel Hitzenberger Germany 14 410 1.3× 96 0.5× 54 0.7× 29 0.5× 34 0.6× 23 618
Bryan VanSchouwen Canada 14 650 2.0× 158 0.8× 89 1.1× 101 1.7× 69 1.2× 33 799
Martin Almlöf Sweden 6 411 1.3× 170 0.8× 79 1.0× 19 0.3× 43 0.7× 9 537

Countries citing papers authored by Mike J. Bodkin

Since Specialization
Citations

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

Fields of papers citing papers by Mike J. Bodkin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mike J. Bodkin. 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 Mike J. Bodkin. The network helps show where Mike J. Bodkin may publish in the future.

Co-authorship network of co-authors of Mike J. Bodkin

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

All Works

15 of 15 papers shown
1.
Zuccotto, Fabio, et al.. (2024). Accurate prediction of dynamic protein–ligand binding using P‐score ranking. Journal of Computational Chemistry. 45(20). 1762–1778. 5 indexed citations
2.
Heifetz, Alexander, et al.. (2023). High-Throughput Structure-Based Drug Design (HT-SBDD) Using Drug Docking, Fragment Molecular Orbital Calculations, and Molecular Dynamic Techniques. Methods in molecular biology. 2716. 293–306. 9 indexed citations
3.
Meli, Rocco, Andrew Anighoro, Mike J. Bodkin, Garrett M. Morris, & Philip C. Biggin. (2021). Learning protein-ligand binding affinity with atomic environment vectors. Journal of Cheminformatics. 13(1). 59–59. 50 indexed citations
4.
Heifetz, Alexander, Michelle Southey, Iñaki Morao, et al.. (2020). Analyzing GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method. Methods in molecular biology. 163–175.
5.
Heifetz, Alexander, et al.. (2020). Guiding Medicinal Chemistry with Fragment Molecular Orbital (FMO) Method. Methods in molecular biology. 2114. 37–48. 7 indexed citations
6.
Heifetz, Alexander, Michelle Southey, Iñaki Morao, et al.. (2019). Characterising GPCR–ligand interactions using a fragment molecular orbital-based approach. Current Opinion in Structural Biology. 55. 85–92. 14 indexed citations
7.
Southey, Michelle, et al.. (2019). Ensemble-Based Steered Molecular Dynamics Predicts Relative Residence Time of A2A Receptor Binders. Journal of Chemical Theory and Computation. 15(5). 3316–3330. 40 indexed citations
8.
Heifetz, Alexander, Michelle Southey, Iñaki Morao, Andrea Townsend‐Nicholson, & Mike J. Bodkin. (2017). Computational Methods Used in Hit-to-Lead and Lead Optimization Stages of Structure-Based Drug Discovery. Methods in molecular biology. 1705. 375–394. 21 indexed citations
9.
Chudyk, Ewa I., Matteo Aldeghi, Dmitri G. Fedorov, et al.. (2017). Exploring GPCR-Ligand Interactions with the Fragment Molecular Orbital (FMO) Method. Methods in molecular biology. 1705. 179–195. 14 indexed citations
10.
Morao, Iñaki, Dmitri G. Fedorov, Michelle Southey, et al.. (2017). Rapid and accurate assessment of GPCR–ligand interactions Using the fragment molecular orbital‐based density‐functional tight‐binding method. Journal of Computational Chemistry. 38(23). 1987–1990. 44 indexed citations
11.
Heifetz, Alexander, Matteo Aldeghi, Ewa I. Chudyk, et al.. (2016). Using the fragment molecular orbital method to investigate agonist–orexin-2 receptor interactions. Biochemical Society Transactions. 44(2). 574–581. 24 indexed citations
12.
Heifetz, Alexander, Richard Storer, Gordon McMurray, et al.. (2016). Application of an Integrated GPCR SAR-Modeling Platform To Explain the Activation Selectivity of Human 5-HT2C over 5-HT2B. ACS Chemical Biology. 11(5). 1372–1382. 10 indexed citations
13.
Heifetz, Alexander, Matteo Aldeghi, Colin H. MacKinnon, et al.. (2016). Fragment Molecular Orbital Method Applied to Lead Optimization of Novel Interleukin-2 Inducible T-Cell Kinase (ITK) Inhibitors. Journal of Medicinal Chemistry. 59(9). 4352–4363. 64 indexed citations
14.
Heifetz, Alexander, Gebhard F. X. Schertler, Roland Seifert, et al.. (2015). GPCR structure, function, drug discovery and crystallography: report from Academia-Industry International Conference (UK Royal Society) Chicheley Hall, 1–2 September 2014. Naunyn-Schmiedeberg s Archives of Pharmacology. 388(8). 883–903. 28 indexed citations
15.
Heifetz, Alexander, Ewa I. Chudyk, Matteo Aldeghi, et al.. (2015). The Fragment Molecular Orbital Method Reveals New Insight into the Chemical Nature of GPCR–Ligand Interactions. Journal of Chemical Information and Modeling. 56(1). 159–172. 90 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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2026