David Ryan Koes

8.4k total citations · 3 hit papers
73 papers, 3.7k citations indexed

About

David Ryan Koes is a scholar working on Molecular Biology, Computational Theory and Mathematics and Materials Chemistry. According to data from OpenAlex, David Ryan Koes has authored 73 papers receiving a total of 3.7k indexed citations (citations by other indexed papers that have themselves been cited), including 40 papers in Molecular Biology, 37 papers in Computational Theory and Mathematics and 22 papers in Materials Chemistry. Recurrent topics in David Ryan Koes's work include Computational Drug Discovery Methods (36 papers), Protein Structure and Dynamics (18 papers) and Machine Learning in Materials Science (16 papers). David Ryan Koes is often cited by papers focused on Computational Drug Discovery Methods (36 papers), Protein Structure and Dynamics (18 papers) and Machine Learning in Materials Science (16 papers). David Ryan Koes collaborates with scholars based in United States, Germany and India. David Ryan Koes's co-authors include Carlos J. Camacho, Jocelyn Sunseri, Matthew P. Baumgartner, Nicholas B. Rego, Paul Francoeur, Tomohide Masuda, Matthew Ragoza, Andrew T. McNutt, Rocco Meli and Rishal Aggarwal and has published in prestigious journals such as Nucleic Acids Research, Journal of Biological Chemistry and Bioinformatics.

In The Last Decade

David Ryan Koes

66 papers receiving 3.6k citations

Hit Papers

align trajectories

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.

Lessons Learned in Empirical Scoring with smina from the CSAR 2011 Benchmarking Exercise

2013 741 citations David Ryan Koes, Carlos J. Camacho et al. Journal of Chemical Information and Modeling profile →
GNINA 1.0: molecular docking with deep learning

2021 397 citations Andrew T. McNutt, Paul Francoeur et al. profile →
GNINA 1.3: the next increment in molecular docking with deep learning

2025 19 citations Andrew T. McNutt, David Ryan Koes et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
David Ryan Koes United States	25	2.3k	2.0k	804	482	351	73	3.7k
Jacob D. Durrant United States	33	2.9k 1.3×	1.7k 0.8×	666 0.8×	487 1.0×	336 1.0×	78	4.2k
Xiaoqin Zou United States	30	3.1k 1.4×	1.8k 0.9×	545 0.7×	428 0.9×	219 0.6×	87	4.1k
A. Geoffrey Skillman United States	17	2.5k 1.1×	1.9k 0.9×	614 0.8×	693 1.4×	404 1.2×	26	3.8k
George Papadatos United Kingdom	19	3.0k 1.3×	3.0k 1.5×	793 1.0×	455 0.9×	624 1.8×	27	4.5k
Michael M. Mysinger United States	8	2.8k 1.2×	2.5k 1.3×	684 0.9×	558 1.2×	514 1.5×	8	4.4k
Michał Nowotka United Kingdom	8	2.1k 0.9×	2.2k 1.1×	587 0.7×	353 0.7×	493 1.4×	11	3.3k
Pedro J. Ballester France	34	2.9k 1.3×	2.9k 1.4×	1.1k 1.4×	322 0.7×	406 1.2×	71	4.4k
Calvin Yu‐Chian Chen Taiwan	36	2.7k 1.2×	2.0k 1.0×	503 0.6×	379 0.8×	529 1.5×	147	4.6k
Chang‐Yu Hsieh China	29	2.1k 0.9×	2.3k 1.2×	1.2k 1.5×	574 1.2×	321 0.9×	128	5.0k
Ryan G. Coleman United States	16	2.2k 1.0×	1.6k 0.8×	476 0.6×	357 0.7×	315 0.9×	22	3.4k

Countries citing papers authored by David Ryan Koes

Since Specialization

Citations

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

Fields of papers citing papers by David Ryan Koes

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by David Ryan Koes. 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 David Ryan Koes. The network helps show where David Ryan Koes may publish in the future.

Co-authorship network of co-authors of David Ryan Koes

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Koes, David Ryan, et al.. (2025). GEOM-drugs revisited: toward more chemically accurate benchmarks for 3D molecule generation. Digital Discovery. 4(11). 3282–3291. 2 indexed citations

Arshad, Sanya, Jeremy S. Tilstra, Mark J. Shlomchik, et al.. (2025). Sliding Window Interaction Grammar (SWING): a generalized interaction language model for peptide and protein interactions. Nature Methods. 22(8). 1707–1719. 4 indexed citations

Pirhadi, Somayeh, et al.. (2025). How evolution shaped the structure of steroidogenic cytochrome P450 11A. Journal of Inorganic Biochemistry. 274. 113105–113105.

McNutt, Andrew T., et al.. (2023). Conformer Generation for Structure-Based Drug Design: How Many and How Good?. Journal of Chemical Information and Modeling. 63(21). 6598–6607. 22 indexed citations

Ray, Sutapa, et al.. (2023). Deciphering the Role of Fatty Acid–Metabolizing CYP4F11 in Lung Cancer and Its Potential As a Drug Target. Drug Metabolism and Disposition. 52(2). 69–79. 8 indexed citations

Francoeur, Paul & David Ryan Koes. (2023). Expanding Training Data for Structure-Based Receptor–Ligand Binding Affinity Regression through Imputation of Missing Labels. ACS Omega. 8(44). 41680–41688.

Francoeur, Paul, et al.. (2023). BigBind: Learning from Nonstructural Data for Structure-Based Virtual Screening. Journal of Chemical Information and Modeling. 64(7). 2488–2495. 6 indexed citations

McNutt, Andrew T. & David Ryan Koes. (2023). Open-ComBind: harnessing unlabeled data for improved binding pose prediction. Journal of Computer-Aided Molecular Design. 38(1). 3–3. 1 indexed citations

King, Jonathan & David Ryan Koes. (2023). Interpreting forces as deep learning gradients improves quality of predicted protein structures. Biophysical Journal. 123(17). 2730–2739. 1 indexed citations

10.

Francoeur, Paul & David Ryan Koes. (2021). SolTranNet–A Machine Learning Tool for Fast Aqueous Solubility Prediction. Journal of Chemical Information and Modeling. 61(6). 2530–2536. 62 indexed citations

11.

Francoeur, Paul & David Ryan Koes. (2021). Correction to “SolTranNet—A Machine Learning Tool for Fast Aqueous Solubility Prediction”. Journal of Chemical Information and Modeling. 61(8). 4120–4123. 3 indexed citations

12.

Liu, Peng, et al.. (2020). The 3Dmol.js Learning Environment: A Classroom Response System for 3D Chemical Structures. Journal of Chemical Education. 97(10). 3872–3876. 10 indexed citations

13.

Chen, Lieyang, Anthony Cruz, Steven Ramsey, et al.. (2019). Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening. PLoS ONE. 14(8). e0220113–e0220113. 169 indexed citations

14.

Gau, David, et al.. (2017). Structure-based virtual screening identifies a small-molecule inhibitor of the profilin 1–actin interaction. Journal of Biological Chemistry. 293(7). 2606–2616. 13 indexed citations

15.

Gau, David, Xuemei Zeng, Nathan A. Yates, et al.. (2016). Threonine 89 Is an Important Residue of Profilin-1 That Is Phosphorylatable by Protein Kinase A. PLoS ONE. 11(5). e0156313–e0156313. 11 indexed citations

16.

Koes, David Ryan & John K. Vries. (2016). Error assessment in molecular dynamics trajectories using computed NMR chemical shifts. Computational and Theoretical Chemistry. 1099. 152–166. 1 indexed citations

17.

Koes, David Ryan, et al.. (2015). A Teach-Discover-Treat Application of ZincPharmer: An Online Interactive Pharmacophore Modeling and Virtual Screening Tool. PLoS ONE. 10(8). e0134697–e0134697. 18 indexed citations

18.

Wenskovitch, John, et al.. (2014). FixingTIM: interactive exploration of sequence and structural data to identify functional mutations in protein families. BMC Proceedings. 8(S2). S3–S3. 4 indexed citations

19.

Koes, David Ryan, Kareem Khoury, Yijun Huang, et al.. (2012). Enabling Large-Scale Design, Synthesis and Validation of Small Molecule Protein-Protein Antagonists. PLoS ONE. 7(3). e32839–e32839. 79 indexed citations

20.

Koes, David Ryan & Carlos J. Camacho. (2012). PocketQuery: protein-protein interaction inhibitor starting points from protein-protein interaction structure. Nucleic Acids Research. 40(W1). W387–W392. 68 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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