Jocelyn Sunseri

1.7k total citations · 1 hit paper
8 papers, 996 citations indexed

About

Jocelyn Sunseri is a scholar working on Computational Theory and Mathematics, Molecular Biology and Materials Chemistry. According to data from OpenAlex, Jocelyn Sunseri has authored 8 papers receiving a total of 996 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Computational Theory and Mathematics, 6 papers in Molecular Biology and 5 papers in Materials Chemistry. Recurrent topics in Jocelyn Sunseri's work include Computational Drug Discovery Methods (7 papers), Machine Learning in Materials Science (5 papers) and Protein Structure and Dynamics (3 papers). Jocelyn Sunseri is often cited by papers focused on Computational Drug Discovery Methods (7 papers), Machine Learning in Materials Science (5 papers) and Protein Structure and Dynamics (3 papers). Jocelyn Sunseri collaborates with scholars based in United States, India and Iran. Jocelyn Sunseri's 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 and has published in prestigious journals such as Nucleic Acids Research, Journal of Chemical Information and Modeling and Journal of Computer-Aided Molecular Design.

In The Last Decade

Jocelyn Sunseri

8 papers receiving 972 citations

Hit Papers

GNINA 1.0: molecular docking with deep learning 2021 2026 2022 2024 2021 100 200 300
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. GNINA 1.0: molecular docking with deep learning
    2021 397 citations Andrew T. McNutt, Paul Francoeur et al. Journal of Cheminformatics profile →

Peers

Jocelyn Sunseri
Florian Flachsenberg Germany
Matthew P. Baumgartner United States
Mélaine A. Kuenemann France
Xiaoqin Tan China
Mark Mackey United Kingdom
Eva Nittinger Sweden
John W. Mayfield United States
Khanh Tang United States
Filip Miljković Germany
Weifan Zheng United States
Florian Flachsenberg Germany
Jocelyn Sunseri
Citations per year, relative to Jocelyn Sunseri Jocelyn Sunseri (= 1×) peers Florian Flachsenberg

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
1.
McNutt, Andrew T., Paul Francoeur, Rishal Aggarwal, et al.. (2021). GNINA 1.0: molecular docking with deep learning. Journal of Cheminformatics. 13(1). 43–43. 397 indexed citations breakdown →
2.
Sunseri, Jocelyn & David Ryan Koes. (2020). libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications. Journal of Chemical Information and Modeling. 60(3). 1079–1084. 39 indexed citations
3.
Francoeur, Paul, et al.. (2020). Three-Dimensional Convolutional Neural Networks and a Cross-Docked Data Set for Structure-Based Drug Design. Journal of Chemical Information and Modeling. 60(9). 4200–4215. 175 indexed citations
4.
Sunseri, Jocelyn, Jonathan King, Paul Francoeur, & David Ryan Koes. (2018). Convolutional neural network scoring and minimization in the D3R 2017 community challenge. Journal of Computer-Aided Molecular Design. 33(1). 19–34. 34 indexed citations
5.
Pirhadi, Somayeh, Jocelyn Sunseri, & David Ryan Koes. (2016). Open source molecular modeling. Journal of Molecular Graphics and Modelling. 69. 127–143. 60 indexed citations
6.
Sunseri, Jocelyn & David Ryan Koes. (2016). Pharmit: interactive exploration of chemical space. Nucleic Acids Research. 44(W1). W442–W448. 277 indexed citations
7.
Sunseri, Jocelyn, Matthew Ragoza, Jasmine Collins, & David Ryan Koes. (2016). A D3R prospective evaluation of machine learning for protein-ligand scoring. Journal of Computer-Aided Molecular Design. 30(9). 761–771. 13 indexed citations
8.
Vankayalapati, Hariprasad, et al.. (2006). 341 POSTER The discovery of MP529, a potent and selective aurora kinase inhibitor using CLIMB. European Journal of Cancer Supplements. 4(12). 106–106. 1 indexed citations

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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2026