Michael G. Lerner

1.3k total citations
15 papers, 752 citations indexed

About

Michael G. Lerner is a scholar working on Molecular Biology, Atomic and Molecular Physics, and Optics and Computational Theory and Mathematics. According to data from OpenAlex, Michael G. Lerner has authored 15 papers receiving a total of 752 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Molecular Biology, 3 papers in Atomic and Molecular Physics, and Optics and 3 papers in Computational Theory and Mathematics. Recurrent topics in Michael G. Lerner's work include Protein Structure and Dynamics (5 papers), Lipid Membrane Structure and Behavior (4 papers) and Computational Drug Discovery Methods (3 papers). Michael G. Lerner is often cited by papers focused on Protein Structure and Dynamics (5 papers), Lipid Membrane Structure and Behavior (4 papers) and Computational Drug Discovery Methods (3 papers). Michael G. Lerner collaborates with scholars based in United States, Netherlands and Germany. Michael G. Lerner's co-authors include Heather A. Carlson, Richard W. Pastor, Mark L. Benson, Richard D. Smith, Frank L. H. Brown, Brian A. Camley, Jefferson D. Knight, Joan G. Marcano-Velázquez, Joseph J. Falke and Richard M. Venable and has published in prestigious journals such as Journal of the American Chemical Society, Journal of Clinical Investigation and The Journal of Chemical Physics.

In The Last Decade

Michael G. Lerner

14 papers receiving 747 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael G. Lerner United States 10 651 251 124 104 89 15 752
Z. Nevin Gerek United States 14 508 0.8× 109 0.4× 62 0.5× 178 1.7× 109 1.2× 17 707
Gabriela Barreiro United States 13 689 1.1× 115 0.5× 72 0.6× 80 0.8× 46 0.5× 20 967
Ryan L. Hayes United States 15 810 1.2× 127 0.5× 84 0.7× 134 1.3× 55 0.6× 29 912
Lane Votapka United States 13 614 0.9× 179 0.7× 73 0.6× 84 0.8× 28 0.3× 25 784
Mazen Ahmad Germany 9 468 0.7× 133 0.5× 67 0.5× 122 1.2× 38 0.4× 20 655
Kerry M. Swift United States 13 871 1.3× 79 0.3× 46 0.4× 93 0.9× 71 0.8× 31 1.1k
Michael D. Daily United States 16 781 1.2× 136 0.5× 75 0.6× 282 2.7× 74 0.8× 23 968
Shawn Witham United States 10 559 0.9× 61 0.2× 86 0.7× 120 1.2× 45 0.5× 12 738
Sugyan M. Dixit United States 11 910 1.4× 219 0.9× 124 1.0× 234 2.3× 30 0.3× 18 1.1k
InSuk Joung South Korea 11 470 0.7× 81 0.3× 82 0.7× 186 1.8× 34 0.4× 22 662

Countries citing papers authored by Michael G. Lerner

Since Specialization
Citations

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

Fields of papers citing papers by Michael G. Lerner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Michael G. Lerner. 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 Michael G. Lerner. The network helps show where Michael G. Lerner may publish in the future.

Co-authorship network of co-authors of Michael G. Lerner

This figure shows the co-authorship network connecting the top 25 collaborators of Michael G. Lerner. A scholar is included among the top collaborators of Michael G. Lerner 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 Michael G. Lerner. Michael G. Lerner 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.
Lerner, Michael G., Hildur Knútsdóttir, Fatemeh Shojaeian, et al.. (2025). P4HA1 Mediates Hypoxia-Induced Invasion in Human Pancreatic Cancer Organoids. Cancer Research Communications. 5(5). 881–895. 2 indexed citations
2.
Lerner, Michael G., et al.. (2024). Prioritizing drug targets by perturbing biological network response functions. PLoS Computational Biology. 20(6). e1012195–e1012195. 3 indexed citations
3.
Knútsdóttir, Hildur, Fatemeh Shojaeian, Michael G. Lerner, et al.. (2023). Morphology-guided transcriptomic analysis of human pancreatic cancer organoids reveals microenvironmental signals that enhance invasion. Journal of Clinical Investigation. 133(8). 14 indexed citations
4.
Rai, Ashutosh, et al.. (2016). Molecular Dynamics Investigations of Z[WC] DNA and Its Potential Role in the B to Z-DNA Transition. Biophysical Journal. 110(3). 404a–404a. 1 indexed citations
5.
Venable, Richard M., Helgi I. Ingólfsson, Michael G. Lerner, et al.. (2016). Lipid and Peptide Diffusion in Bilayers: The Saffman–Delbrück Model and Periodic Boundary Conditions. The Journal of Physical Chemistry B. 121(15). 3443–3457. 93 indexed citations
6.
Camley, Brian A., Michael G. Lerner, Richard W. Pastor, & Frank L. H. Brown. (2015). Strong influence of periodic boundary conditions on lateral diffusion in lipid bilayer membranes. The Journal of Chemical Physics. 143(24). 243113–243113. 70 indexed citations
7.
Levine, Zachary A., Richard M. Venable, Max C. Watson, et al.. (2014). Determination of Biomembrane Bending Moduli in Fully Atomistic Simulations. Journal of the American Chemical Society. 136(39). 13582–13585. 84 indexed citations
8.
Pickard, Frank C., et al.. (2014). Web-Based Computational Chemistry Education with CHARMMing II: Coarse-Grained Protein Folding. PLoS Computational Biology. 10(7). e1003738–e1003738. 5 indexed citations
9.
Knight, Jefferson D., Michael G. Lerner, Joan G. Marcano-Velázquez, Richard W. Pastor, & Joseph J. Falke. (2010). Single Molecule Diffusion of Membrane-Bound Proteins: Window into Lipid Contacts and Bilayer Dynamics. Biophysical Journal. 99(9). 2879–2887. 139 indexed citations
10.
Lerner, Michael G., Kristin L. Meagher, & Heather A. Carlson. (2008). Automated clustering of probe molecules from solvent mapping of protein surfaces: new algorithms applied to hot-spot mapping and structure-based drug design. Journal of Computer-Aided Molecular Design. 22(10). 727–736. 11 indexed citations
11.
Bowman, Anna L., Michael G. Lerner, & Heather A. Carlson. (2007). Protein Flexibility and Species Specificity in Structure-Based Drug Discovery:  Dihydrofolate Reductase as a Test System. Journal of the American Chemical Society. 129(12). 3634–3640. 39 indexed citations
12.
Lerner, Michael G., Anna L. Bowman, & Heather A. Carlson. (2007). Incorporating Dynamics in E. coli Dihydrofolate Reductase Enhances Structure-Based Drug Discovery. Journal of Chemical Information and Modeling. 47(6). 2358–2365. 25 indexed citations
13.
Meagher, Kristin L., Michael G. Lerner, & Heather A. Carlson. (2006). Refining the Multiple Protein Structure Pharmacophore Method:  Consistency across Three Independent HIV-1 Protease Models. Journal of Medicinal Chemistry. 49(12). 3478–3484. 22 indexed citations
14.
Lerner, Michael G., et al.. (2006). Auswirkungen von neuen Fahrzeugkonzepten auf die Infrastruktur des Bundesfernstrassennetzes. Schlussbericht.
15.
Benson, Mark L., et al.. (2005). Binding MOAD (Mother Of All Databases). Proteins Structure Function and Bioinformatics. 60(3). 333–340. 244 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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