John W. Mayfield

1.6k citations
3 papers · 936 indexed · 2 hit papers · h-index 3
Topics
Computational Drug Discovery Methods (3 papers)Analytical Chemistry and Chromatography (2 papers)Microbial Natural Products and Biosynthesis (1 paper)

In The Last Decade

John W. Mayfield

3 papers receiving 914 citations

Hit Papers

ZINC20—A Free Ultralarge-Scale Chemical Database for Liga...201720262020202320202017100200300400500

Peers

John W. Mayfield
Comparison fields: 5 of 120
  • Computational Theory and Mathematics 581
  • Molecular Biology 542
  • Materials Chemistry 207
  • Spectroscopy 104
  • Pharmacology 102
Replace Eva Nittinger with:
Eva Nittinger Sweden
Valery Tkachenko United States
Eloy Félix United Kingdom
Ed Griffen United Kingdom
Jonathan Alvarsson Sweden
Veerabahu Shanmugasundaram United States
Tailong Lei China
Florian Flachsenberg Germany
Dilyana Dimova Germany
Mark Mackey United Kingdom
John W. Mayfield relative to Eva Nittinger Sweden Eva Nittinger's profile →
Citations per field
00.5×1.7×
Eva Nittinger · 1×
Citations per year

Countries citing papers authored by John W. Mayfield

Since Specialization
Citations

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

Fields of papers citing papers by John W. Mayfield

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John W. Mayfield. 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 John W. Mayfield. The network helps show where John W. Mayfield may publish in the future.

Co-authorship network of co-authors of John W. Mayfield

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

All Works

3 of 3 papers shown
#WorkIndexed citations
1
ZINC20—A Free Ultralarge-Scale Chemical Database for Ligand Discoverybreakdown →
556
2 14
3
The Chemistry Development Kit (CDK) v2.0: atom typing, depiction, molecular formulas, and substructure searchingbreakdown →
366

About John W. Mayfield

John W. Mayfield is a scholar working on Computational Theory and Mathematics, Spectroscopy and Physical and Theoretical Chemistry, having authored 3 papers that have together received 936 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (3 papers), Analytical Chemistry and Chromatography (2 papers) and Microbial Natural Products and Biosynthesis (1 paper). The work is most often cited by research in Computational Theory and Mathematics (581 citations), Molecular Biology (542 citations) and Spectroscopy (104 citations). John W. Mayfield has collaborated with scholars based in United States, Netherlands and Sweden. Frequent co-authors include Khanh Tang, Jennifer J. Young, Roger A. Sayle, John J. Irwin, Yurii S. Moroz, Chris T. Evelo, Tomáš Pluskal, Nina Jeliazkova, Rajarshi Guha and Arvid Berg. Their work appears in journals such as Journal of Chemical Information and Modeling and Journal of Cheminformatics.

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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