Jocelyn Sunseri

1.7k citations
8 papers · 996 indexed · 1 hit paper · h-index 7
Topics
Computational Drug Discovery Methods (7 papers)Machine Learning in Materials Science (5 papers)Protein Structure and Dynamics (3 papers)
Partner nations
United StatesIndiaIran

In The Last Decade

Jocelyn Sunseri

8 papers receiving 972 citations

Hit Papers

GNINA 1.0: molecular docking with deep learning20212026202220242021100200300

Peers

Jocelyn Sunseri
Comparison fields: 5 of 115
  • Molecular Biology 637
  • Computational Theory and Mathematics 629
  • Materials Chemistry 240
  • Organic Chemistry 130
  • Pharmacology 87
Replace Florian Flachsenberg with:
Florian Flachsenberg Germany
Matthew P. Baumgartner United States
John W. Mayfield United States
Eva Nittinger Sweden
Xiaoqin Tan China
Mélaine A. Kuenemann France
Mark Mackey United Kingdom
Khanh Tang United States
Weifan Zheng United States
Eloy Félix United Kingdom
Jocelyn Sunseri relative to Florian Flachsenberg Germany Florian Flachsenberg's profile →
Citations per field
00.5×
Florian Flachsenberg · 1×
Citations per year

Countries citing papers authored by Jocelyn Sunseri

Since Specialization
Citations

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

Fields of papers citing papers by Jocelyn Sunseri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Jocelyn Sunseri

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

All Works

8 of 8 papers shown
#WorkIndexed citations
1
GNINA 1.0: molecular docking with deep learningbreakdown →
397
2 39
3 175
4 34
5 60
6 277
7 13
8 1

About Jocelyn Sunseri

Jocelyn Sunseri is a scholar working on Computational Theory and Mathematics, Materials Chemistry and Molecular Biology, having authored 8 papers that have together received 996 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (7 papers), Machine Learning in Materials Science (5 papers) and Protein Structure and Dynamics (3 papers). The work is most often cited by research in Computational Theory and Mathematics (629 citations), Molecular Biology (637 citations) and Materials Chemistry (240 citations). Jocelyn Sunseri has collaborated with scholars based in United States, India and Iran. Frequent co-authors include David Ryan Koes, Paul Francoeur, Tomohide Masuda, Matthew Ragoza, Rishal Aggarwal, Rocco Meli, Andrew T. McNutt, I. M. Snyder, Somayeh Pirhadi and Jonathan King. Their work appears in journals such as Nucleic Acids Research, Journal of Chemical Information and Modeling and Journal of Computer-Aided Molecular Design.

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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