Frank Lovering

7.1k citations
28 papers · 5.2k indexed · 2 hit papers · h-index 19
Topics
Computational Drug Discovery Methods (7 papers)Chemical Synthesis and Analysis (6 papers)Protein Kinase Regulation and GTPase Signaling (6 papers)

In The Last Decade

Frank Lovering

28 papers receiving 5.0k citations

Hit Papers

Escape from Flatland: Increasing Saturation as an Approac...20092026201420202009201210002.0k3.0k

Peers

Frank Lovering
Comparison fields: 5 of 110
  • Organic Chemistry 3.7k
  • Molecular Biology 1.6k
  • Computational Theory and Mathematics 603
  • Inorganic Chemistry 560
  • Pharmaceutical Science 482
Replace Jack Bikker with:
Jack Bikker United States
Christine Humblet United States
Gunda I. Georg United States
Simon J. F. Macdonald United Kingdom
Christopher J. Helal United States
Rodolfo Lavilla Spain
Hing L. Sham United States
Antonia F. Stepan United States
Allan M. Jordan United Kingdom
Paul Helquist United States
Frank Lovering relative to Jack Bikker United States Jack Bikker's profile →
Citations per field
00.5×1.5×1.8×
Jack Bikker · 1×
Citations per year

Countries citing papers authored by Frank Lovering

Since Specialization
Citations

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

Fields of papers citing papers by Frank Lovering

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Frank Lovering

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1 38
2 122
3 35
4 17
5 30
6 8
7 24
8 69
9 12
10 34
11
Inhibition of Cytosolic Phospholipase A 2 α: Hit to Lead Optimization
10
12 6
13 37
14 27
15 60
16 18
17 18
18 42
19 2
20 42

About Frank Lovering

Frank Lovering is a scholar working on Immunology and Allergy, Pharmacology and Computational Theory and Mathematics, having authored 28 papers that have together received 5.2k indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (7 papers), Chemical Synthesis and Analysis (6 papers) and Protein Kinase Regulation and GTPase Signaling (6 papers). The work is most often cited by research in Organic Chemistry (3.7k citations), Pharmaceutical Science (482 citations) and Inorganic Chemistry (560 citations). Frank Lovering has collaborated with scholars based in United States, United Kingdom and Canada. Frequent co-authors include Christine Humblet, Jack Bikker, William H. Pearson, R. Aldrin Denny, Ray Unwalla, A. Richard Chamberlin, Richard J. Bridges, Xing Li, Huanyu Zhou and Mark E. Bunnage. Their work appears in journals such as Journal of the American Chemical Society, Scientific Reports and Journal of Medicinal Chemistry.

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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