Ryan Hard

654 total citations
10 papers, 520 citations indexed

About

Ryan Hard is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Oncology. According to data from OpenAlex, Ryan Hard has authored 10 papers receiving a total of 520 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Molecular Biology, 3 papers in Radiology, Nuclear Medicine and Imaging and 2 papers in Oncology. Recurrent topics in Ryan Hard's work include vaccines and immunoinformatics approaches (3 papers), Monoclonal and Polyclonal Antibodies Research (3 papers) and Ubiquitin and proteasome pathways (2 papers). Ryan Hard is often cited by papers focused on vaccines and immunoinformatics approaches (3 papers), Monoclonal and Polyclonal Antibodies Research (3 papers) and Ubiquitin and proteasome pathways (2 papers). Ryan Hard collaborates with scholars based in United States and United Kingdom. Ryan Hard's co-authors include Dehua Pei, Ziqing Qian, Agnieszka Martyna, Jiang Wang, Christopher C. Coss, Mitch A. Phelps, Jeremy S. Rossman, Wenlong Lian, Jonathan R. LaRochelle and Amy Barrios and has published in prestigious journals such as Nature Communications, Biochemistry and Science Advances.

In The Last Decade

Ryan Hard

9 papers receiving 517 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Ryan Hard United States	8	465	95	65	49	48	10	520
Leila Peraro United States	11	512 1.1×	77 0.8×	59 0.9×	46 0.9×	64 1.3×	14	612
Misao Akishiba Japan	9	482 1.0×	71 0.7×	60 0.9×	29 0.6×	82 1.7×	14	542
Kristina Najjar United States	8	492 1.1×	82 0.9×	23 0.4×	40 0.8×	89 1.9×	10	553
Katri Rosenthal-Aizman Sweden	5	423 0.9×	94 1.0×	24 0.4×	50 1.0×	41 0.9×	6	512
Hao‐Hsin Yu Japan	6	380 0.8×	70 0.7×	36 0.6×	33 0.7×	63 1.3×	6	428
Jan Hoyer Germany	6	346 0.7×	61 0.6×	14 0.2×	24 0.5×	57 1.2×	10	401
Gilles Moulay France	12	281 0.6×	134 1.4×	42 0.6×	37 0.8×	58 1.2×	14	460
Caroline Palm‐Apergi Sweden	11	750 1.6×	185 1.9×	17 0.3×	66 1.3×	84 1.8×	19	852
Ivana Ruseska Austria	4	311 0.7×	78 0.8×	18 0.3×	32 0.7×	42 0.9×	10	388
Anne K. Ludwig Germany	6	377 0.8×	16 0.2×	62 1.0×	29 0.6×	67 1.4×	9	442

Countries citing papers authored by Ryan Hard

Since Specialization

Citations

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

Fields of papers citing papers by Ryan Hard

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ryan Hard

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

All Works

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10 of 10 papers shown

1.

Parkinson, Jonathan, et al.. (2025). RESP2: An Uncertainty Aware Multi‐Target Multi‐Property Optimization AI Pipeline for Antibody Discovery. Advanced Science. 12(38). e04350–e04350.

2.

Parkinson, Jonathan, Ryan Hard, & Wei Wang. (2023). The RESP AI model accelerates the identification of tight-binding antibodies. Nature Communications. 14(1). 454–454. 26 indexed citations

3.

Parkinson, Jonathan, Ryan Hard, Richard I. Ainsworth, & Wei Wang. (2022). Engineering human JMJD2A tudor domains for an improved understanding of histone peptide recognition. Proteins Structure Function and Bioinformatics. 91(1). 32–46. 2 indexed citations

4.

Parkinson, Jonathan, Ryan Hard, Richard I. Ainsworth, Nan Li, & Wei Wang. (2020). Engineering a Histone Reader Protein by Combining Directed Evolution, Sequencing, and Neural Network Based Ordinal Regression. Journal of Chemical Information and Modeling. 60(8). 3992–4004. 7 indexed citations

5.

Hard, Ryan, Nan Li, Wei He, et al.. (2018). Deciphering and engineering chromodomain-methyllysine peptide recognition. Science Advances. 4(11). eaau1447–eaau1447. 18 indexed citations

6.

Qian, Ziqing, Agnieszka Martyna, Ryan Hard, et al.. (2016). Discovery and Mechanism of Highly Efficient Cyclic Cell-Penetrating Peptides. Biochemistry. 55(18). 2601–2612. 243 indexed citations

7.

Qian, Ziqing, Jonathan R. LaRochelle, Wenlong Lian, et al.. (2014). Early Endosomal Escape of a Cyclic Cell-Penetrating Peptide Allows Effective Cytosolic Cargo Delivery. Biochemistry. 53(24). 4034–4046. 145 indexed citations

8.

Zhang, Jinjin, Chunhua Yuan, Ryan Hard, et al.. (2011). Simultaneous Binding of Two Peptidyl Ligands by a Src Homology 2 Domain. Biochemistry. 50(35). 7637–7646. 22 indexed citations

9.

Hard, Ryan, Jiangxin Liu, Juan Shen, Pei Zhou, & Dehua Pei. (2010). HDAC6 and Ubp-M BUZ Domains Recognize Specific C-Terminal Sequences of Proteins. Biochemistry. 49(50). 10737–10746. 27 indexed citations

10.

Shepherd, Tyson R., Ryan Hard, Ann Murray, Dehua Pei, & Ernesto J. Fuentes. (2010). Distinct Ligand Specificity of the Tiam1 and Tiam2 PDZ Domains. Biochemistry. 50(8). 1296–1308. 30 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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