Andrew Dalke

72.4k citations
13 papers · 56.0k indexed · 2 hit papers · h-index 9

Andrew Dalke

13 papers receiving 55.5k citations

Hit Papers

Biopython: freely available Python tools for comp...3.5k199620262006201610.0k20.0k30.0k40.0k50.0k

Peers

Andrew Dalke
Comparison fields: 5 of 210
  • Molecular Biology 26.7k
  • Physical and Theoretical Chemistry 3.4k
  • Materials Chemistry 13.0k
  • Organic Chemistry 7.5k
  • Catalysis 1.8k
Replace William Humphrey with:
William Humphrey United States
David van der Spoel Sweden
Erik Lindahl Sweden
Jayaraman Chandrasekhar India
Lee G. Pedersen United States
Kenneth M. Merz United States
Tom Darden United States
Wilfred F. van Gunsteren Switzerland
Piotr Cieplak United States
David A. Case United States
Andrew Dalke relative to William Humphrey United States William Humphrey's profile →
Citations per field
00.5×1.5×
William Humphrey · 1×
Citations per year

Countries citing papers authored by Andrew Dalke

Since Specialization
Citations

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

Fields of papers citing papers by Andrew Dalke

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

13 of 13 papers shown
#Work
1 20201
2 201935
3 201873
4 201315
5 201331
6
Biopython: freely available Python tools for computational molecular biology and bioinformaticsbreakdown →
20093496
7 20081
8 200210
9
NAMD Version 2.4
20023
10
Using Tcl for molecular visualization and analysis.
19975
11
VMD: Visual molecular dynamicsbreakdown →
199651916
12 1996420
13 199540

About Andrew Dalke

Andrew Dalke is a scholar working on Computational Theory and Mathematics, Computer Science Applications and Pharmaceutical Science, having authored 13 papers that have together received 56.0k indexed citations. Recurring topics across this work include Protein Structure and Dynamics (6 papers), Computational Drug Discovery Methods (5 papers), Metabolomics and Mass Spectrometry Studies (3 papers), Enzyme Structure and Function (3 papers), Machine Learning in Materials Science (2 papers), Analytical Chemistry and Chromatography (2 papers), Bioinformatics and Genomic Networks (2 papers) and Genomics and Phylogenetic Studies (2 papers). The work is most often cited by research in Molecular Biology (26.7k citations), Physical and Theoretical Chemistry (3.4k citations) and Materials Chemistry (13.0k citations). Andrew Dalke has collaborated with scholars based in United States, United Kingdom and Sweden. Frequent co-authors include Klaus Schulten, William Humphrey, Brad Chapman, Bartek Wilczyński, Peter Cock, Cymon J. Cox, Frank Kauff, Michiel de Hoon, Iddo Friedberg and Tiago Antão. Their work appears in journals such as Journal of Cheminformatics, Computer Physics Communications, Bioinformatics, Journal of Chemical Information and Modeling and Chemistry Central Journal.

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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