Matthew Ragoza

1.1k citations
6 papers · 599 indexed · 1 hit paper · h-index 5
Topics
Computational Drug Discovery Methods (4 papers)Protein Structure and Dynamics (3 papers)Machine Learning in Materials Science (2 papers)

In The Last Decade

Matthew Ragoza

6 papers receiving 581 citations

Hit Papers

GNINA 1.0: molecular docking with deep learning20212026202220242021100200300

Peers

Matthew Ragoza
Comparison fields: 5 of 100
  • Molecular Biology 392
  • Computational Theory and Mathematics 372
  • Materials Chemistry 176
  • Organic Chemistry 57
  • Pharmacology 56
Replace Rocco Meli with:
Rocco Meli United Kingdom
Paul Francoeur United States
Tomohide Masuda Japan
Rishal Aggarwal India
Khanh Tang United States
Andrew T. McNutt United States
Arthur Garon Austria
Kateryna A. Tolmachova Switzerland
Harris Ioannidis United Kingdom
Sergio Ruiz‐Carmona Spain
Matthew Ragoza relative to Rocco Meli United Kingdom Rocco Meli's profile →
Citations per field
00.5×4.3×
Rocco Meli · 1×
Citations per year

Countries citing papers authored by Matthew Ragoza

Since Specialization
Citations

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

Fields of papers citing papers by Matthew Ragoza

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Ragoza

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

All Works

6 of 6 papers shown
#WorkIndexed citations
1 1
2 12
3 111
4
GNINA 1.0: molecular docking with deep learningbreakdown →
397
5 65
6 13

About Matthew Ragoza

Matthew Ragoza is a scholar working on Computational Theory and Mathematics, Biophysics and Molecular Biology, having authored 6 papers that have together received 599 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (4 papers), Protein Structure and Dynamics (3 papers) and Machine Learning in Materials Science (2 papers). The work is most often cited by research in Computational Theory and Mathematics (372 citations), Molecular Biology (392 citations) and Materials Chemistry (176 citations). Matthew Ragoza has collaborated with scholars based in United States, Australia and India. Frequent co-authors include David Ryan Koes, Tomohide Masuda, Jocelyn Sunseri, Andrew T. McNutt, Rishal Aggarwal, Rocco Meli, Paul Francoeur, Jasmine Collins, Kayhan Batmanghelich and Mingming Gong. Their work appears in journals such as Chemical Science, Journal of Computer-Aided Molecular Design and Lecture notes in computer science.

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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