Neil P. King

16.0k citations
71 papers · 5.2k indexed · 8 hit papers · h-index 26

Impact in

Papers in

Neil P. King

68 papers receiving 5.1k citations

Hit Papers

Top-down design of protein architectures with reinforcement learning 2023 · 76 citations
7620122026201620212505007501000

Peers

Neil P. King
Comparison fields: 5 of 140
  • Infectious Diseases 1.8k
  • Structural Biology 82
  • Biomaterials 603
  • Molecular Biology 3.0k
  • Ecology 1.1k
Replace Mark Howarth with:
Mark Howarth United Kingdom
Peter G. Stockley United Kingdom
Joost Snijder Netherlands
Sarah J. Butcher Finland
Yizhi Jane Tao United States
Adam Zlotnick United States
C. Cheng Kao United States
Joel Quispe United States
Sanjay Tyagi United States
Wouter H. Roos Netherlands
Neil P. King relative to Mark Howarth United Kingdom Mark Howarth's profile →
Citations per field
00.5×3.0×
Mark Howarth · 1×
Citations per year

Countries citing papers authored by Neil P. King

Since Specialization
Citations

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

Fields of papers citing papers by Neil P. King

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Neil P. King. 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 Neil P. King. The network helps show where Neil P. King may publish in the future.

Co-authors

The 25 scholars most cited alongside Neil P. King, 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 Neil P. King Line = papers co-authored together Neil P. King links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20251
2 20250
3 202510
4 202416
5 20235
6 202316
7 202311
8
Top-down design of protein architectures with reinforcement learning
Hit paper breakdown →
202376
9 20234
10 202234
11 202224
12 20228
13 202120
14 202116
15 2020152
16 202044
17 20199
18 201849
19
Computational Design of Self-Assembling Protein Nanomaterials with Atomic Level Accuracy
Hit paper breakdown →
2012522
20 2010113

About Neil P. King

Neil P. King is a scholar working on Structural Biology, Ecology, Infectious Diseases, Immunology and Molecular Biology, having authored 71 papers that have together received 5.2k indexed citations. Recurring topics across this work include Bacteriophages and microbial interactions (23 papers), RNA and protein synthesis mechanisms (16 papers), Protein Structure and Dynamics (11 papers), SARS-CoV-2 and COVID-19 Research (11 papers), Monoclonal and Polyclonal Antibodies Research (10 papers), Enzyme Structure and Function (6 papers), Virus-based gene therapy research (5 papers) and Immunotherapy and Immune Responses (5 papers). The work is most often cited by research in Infectious Diseases (1.8k citations), Structural Biology (82 citations), Biomaterials (603 citations), Molecular Biology (3.0k citations) and Ecology (1.1k citations). Neil P. King has collaborated with scholars based in United States, Netherlands and United Kingdom. Frequent co-authors include Todd O. Yeates, David Baker, William Sheffler, Tamir Gonen, Daniel Ellis, Jesse D. Bloom, Katharine H. D. Crawford, Adam S. Dingens, Jacob B. Bale and David Veesler. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences, Nature Communications, Science and Cell Reports.

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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