Jeffrey A. Ruffolo

1.8k total citations · 3 hit papers
14 papers, 712 citations indexed

About

Jeffrey A. Ruffolo is a scholar working on Molecular Biology, Radiology, Nuclear Medicine and Imaging and Immunology. According to data from OpenAlex, Jeffrey A. Ruffolo has authored 14 papers receiving a total of 712 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Molecular Biology, 10 papers in Radiology, Nuclear Medicine and Imaging and 2 papers in Immunology. Recurrent topics in Jeffrey A. Ruffolo's work include Monoclonal and Polyclonal Antibodies Research (10 papers), Glycosylation and Glycoproteins Research (7 papers) and vaccines and immunoinformatics approaches (4 papers). Jeffrey A. Ruffolo is often cited by papers focused on Monoclonal and Polyclonal Antibodies Research (10 papers), Glycosylation and Glycoproteins Research (7 papers) and vaccines and immunoinformatics approaches (4 papers). Jeffrey A. Ruffolo collaborates with scholars based in United States, Mongolia and Switzerland. Jeffrey A. Ruffolo's co-authors include Jeffrey J. Gray, Sai Pooja Mahajan, Ali Madani, Jeremias Sulam, Lee‐Shin Chu, Erik Nijkamp, Eli N. Weinstein, Nikhil Naik, Richard W. Shuai and Ameya Harmalkar and has published in prestigious journals such as Nature, Nature Communications and Nature Biotechnology.

In The Last Decade

Jeffrey A. Ruffolo

14 papers receiving 683 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.

ProGen2: Exploring the boundaries of protein language models

2023 169 citations Erik Nijkamp, Jeffrey A. Ruffolo et al. Cell Systems profile →
Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies

2023 162 citations Jeffrey A. Ruffolo, Lee‐Shin Chu et al. Nature Communications profile →
Design of highly functional genome editors by modelling CRISPR–Cas sequences

2025 21 citations Jeffrey A. Ruffolo, Stephen Nayfach et al. Nature profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Jeffrey A. Ruffolo United States	11	579	349	79	60	54	14	712
Sai Pooja Mahajan United States	9	360 0.6×	217 0.6×	58 0.7×	64 1.1×	56 1.0×	12	455
Rahmad Akbar Norway	12	338 0.6×	222 0.6×	139 1.8×	22 0.4×	51 0.9×	27	466
Jared Adolf‐Bryfogle United States	12	546 0.9×	398 1.1×	125 1.6×	43 0.7×	44 0.8×	18	714
Angelika Fuchs Germany	13	800 1.4×	401 1.1×	140 1.8×	34 0.6×	35 0.6×	16	961
Puneet Rawat India	13	331 0.6×	152 0.4×	46 0.6×	21 0.3×	65 1.2×	27	424
Hristo L. Svilenov Germany	14	327 0.6×	203 0.6×	56 0.7×	30 0.5×	23 0.4×	31	401
Cristian Regep United Kingdom	5	240 0.4×	167 0.5×	78 1.0×	47 0.8×	78 1.4×	5	385
Fergus Boyles United Kingdom	6	328 0.6×	228 0.7×	74 0.9×	44 0.7×	107 2.0×	7	426
Francesco Ambrosetti Netherlands	7	328 0.6×	191 0.5×	60 0.8×	50 0.8×	47 0.9×	9	409
Theodora U. J. Bruun United States	10	442 0.8×	174 0.5×	83 1.1×	21 0.3×	16 0.3×	22	772

Countries citing papers authored by Jeffrey A. Ruffolo

Since Specialization

Citations

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

Fields of papers citing papers by Jeffrey A. Ruffolo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jeffrey A. Ruffolo. 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 Jeffrey A. Ruffolo. The network helps show where Jeffrey A. Ruffolo may publish in the future.

Co-authorship network of co-authors of Jeffrey A. Ruffolo

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

All Works

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14 of 14 papers shown

Ruffolo, Jeffrey A., Stephen Nayfach, Joseph P. Gallagher, et al.. (2025). Design of highly functional genome editors by modelling CRISPR–Cas sequences. Nature. 645(8080). 518–525. 21 indexed citations breakdown →

Hutchinson, Mark R., Jeffrey A. Ruffolo, Michael J. Iannotti, et al.. (2024). Toward enhancement of antibody thermostability and affinity by computational design in the absence of antigen. mAbs. 16(1). 2362775–2362775. 10 indexed citations

Ruffolo, Jeffrey A. & Ali Madani. (2024). Designing proteins with language models. Nature Biotechnology. 42(2). 200–202. 42 indexed citations

Shuai, Richard W., Jeffrey A. Ruffolo, & Jeffrey J. Gray. (2023). IgLM: Infilling language modeling for antibody sequence design. Cell Systems. 14(11). 979–989.e4. 58 indexed citations

Ruffolo, Jeffrey A., Lee‐Shin Chu, Sai Pooja Mahajan, & Jeffrey J. Gray. (2023). Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. Nature Communications. 14(1). 2389–2389. 162 indexed citations breakdown →

Chu, Lee‐Shin, Jeffrey A. Ruffolo, Ameya Harmalkar, & Jeffrey J. Gray. (2023). Flexible protein–protein docking with a multitrack iterative transformer. Protein Science. 33(2). e4862–e4862. 17 indexed citations

Nijkamp, Erik, Jeffrey A. Ruffolo, Eli N. Weinstein, Nikhil Naik, & Ali Madani. (2023). ProGen2: Exploring the boundaries of protein language models. Cell Systems. 14(11). 968–978.e3. 169 indexed citations breakdown →

Ruffolo, Jeffrey A., et al.. (2022). Simultaneous prediction of antibody backbone and side-chain conformations with deep learning. PLoS ONE. 17(6). e0258173–e0258173. 10 indexed citations

Mahajan, Sai Pooja, Jeffrey A. Ruffolo, Rahel Frick, & Jeffrey J. Gray. (2022). Hallucinating structure-conditioned antibody libraries for target-specific binders. Frontiers in Immunology. 13. 999034–999034. 11 indexed citations

10.

Mahajan, Sai Pooja, Jeffrey A. Ruffolo, Rahel Frick, & Jeffrey J. Gray. (2022). Towards deep learning models for target-specific antibody design. Biophysical Journal. 121(3). 528a–528a. 1 indexed citations

11.

Agatemor, Christian, Jeffrey A. Ruffolo, Jeremy Post, et al.. (2022). A versatile design platform for glycoengineering therapeutic antibodies. mAbs. 14(1). 2095704–2095704. 4 indexed citations

12.

Ruffolo, Jeffrey A. & Jeffrey J. Gray. (2022). Fast, accurate antibody structure prediction from deep learning on massive set of natural antibodies. Biophysical Journal. 121(3). 155a–156a. 27 indexed citations

13.

Ruffolo, Jeffrey A., Jeremias Sulam, & Jeffrey J. Gray. (2021). Antibody structure prediction using interpretable deep learning. Patterns. 3(2). 100406–100406. 122 indexed citations

14.

Ruffolo, Jeffrey A., et al.. (2020). Geometric potentials from deep learning improve prediction of CDR H3 loop structures. Bioinformatics. 36(Supplement_1). i268–i275. 58 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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