Mark McGann

11 papers receiving 1.4k citations

Mark McGann's Hit Papers

FRED Pose Prediction and Virtual Screening Accuracy 2011 · 598 citations
5980+5+10Years since publication100200300400500

Peers

Mark McGann
Comparison fields: 5 of 114
  • Computational Theory and Mathematics 581
  • Molecular Biology 865
  • Organic Chemistry 300
  • Toxicology 29
  • Pharmacology 136
Replace Gregory A. Ross with:
Gregory A. Ross United States
Thompson N. Doman United States
Gregory Sliwoski United States
Edward W. Lowe United States
Sayan Mondal United States
Miriam Sgobba Italy
Matthew T. Stahl United States
Mattia Sturlese Italy
David L. Pincus United States
Kai Zhu United States
Mark McGann relative to Gregory A. Ross United States Gregory A. Ross's profile →
Citations per field
00.5×3.1×
Gregory A. Ross · 1×
Citations per year

Countries citing papers authored by Mark McGann

Since Specialization
Citations

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

Fields of papers citing papers by Mark McGann

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 9 scholars most cited alongside Mark McGann, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mark McGann Line = papers co-authored together Mark McGann links everyone, so they are left out of the graph.

All Works

11 of 11 papers shown
#Work
1
FRED Pose Prediction and Virtual Screening Accuracy
Hit paper breakdown →
2011598
2 2012382
3 2002372
4 199915
5 201513
6 20217
7 19997
8 19996
9 19984
10 19982
11 20221

About Mark McGann

Mark McGann is a scholar working on Molecular Biology, Computational Theory and Mathematics, Polymers and Plastics, Materials Chemistry and Mechanics of Materials, having authored 11 papers that have together received 1.4k indexed citations. Recurring topics across this work include Polymer crystallization and properties (4 papers), Computational Drug Discovery Methods (4 papers), Protein Structure and Dynamics (3 papers), Enzyme Structure and Function (2 papers), Mechanical Behavior of Composites (2 papers), Phase Equilibria and Thermodynamics (2 papers), Rheology and Fluid Dynamics Studies (2 papers) and Imbalanced Data Classification Techniques (1 paper). The work is most often cited by research in Computational Theory and Mathematics (581 citations), Molecular Biology (865 citations), Organic Chemistry (300 citations), Toxicology (29 citations) and Pharmacology (136 citations). Mark McGann has collaborated with scholars based in United States, Russia and United Kingdom. Frequent co-authors include Anthony Nicholls, Harold R. Almond, Frank K. Brown, Jennifer Grant, Daniel J. Lacks, Istvan Enyedy, Shifan Ma, Yankang Jing and Sándor Vajda. Their work appears in journals such as The Journal of Physical Chemistry B, Journal of Computer-Aided Molecular Design, Journal of Chemical Information and Modeling, The Journal of Chemical Physics and Macromolecules.

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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