Fergus Boyles

895 total citations · 1 hit paper
7 papers, 426 citations indexed

About

Fergus Boyles is a scholar working on Molecular Biology, Computational Theory and Mathematics and Pharmacology. According to data from OpenAlex, Fergus Boyles has authored 7 papers receiving a total of 426 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 3 papers in Computational Theory and Mathematics and 2 papers in Pharmacology. Recurrent topics in Fergus Boyles's work include Protein Structure and Dynamics (3 papers), Computational Drug Discovery Methods (3 papers) and vaccines and immunoinformatics approaches (2 papers). Fergus Boyles is often cited by papers focused on Protein Structure and Dynamics (3 papers), Computational Drug Discovery Methods (3 papers) and vaccines and immunoinformatics approaches (2 papers). Fergus Boyles collaborates with scholars based in United Kingdom and Germany. Fergus Boyles's co-authors include Charlotte M. Deane, Tobias Hegelund Olsen, Garrett M. Morris, Wing Ki Wong, Guy Georges, Alexander Bujotzek, Brennan Abanades, G. J. DURANT, Kristian Birchall and Brian D. Marsden and has published in prestigious journals such as Nucleic Acids Research, Bioinformatics and Protein Science.

In The Last Decade

Fergus Boyles

7 papers receiving 403 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.

ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins

2023 150 citations Brennan Abanades, Wing Ki Wong et al. Communications Biology profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Fergus Boyles United Kingdom	6	328	228	107	74	44	7	426
Shinji Soga Japan	7	324 1.0×	169 0.7×	91 0.9×	55 0.7×	29 0.7×	15	416
Rahmad Akbar Norway	12	338 1.0×	222 1.0×	51 0.5×	139 1.9×	22 0.5×	27	466
Sai Pooja Mahajan United States	9	360 1.1×	217 1.0×	56 0.5×	58 0.8×	64 1.5×	12	455
Jeffrey A. Ruffolo United States	11	579 1.8×	349 1.5×	54 0.5×	79 1.1×	60 1.4×	14	712
Francesco Ambrosetti Netherlands	7	328 1.0×	191 0.8×	47 0.4×	60 0.8×	50 1.1×	9	409
Nick Jarvik Canada	10	285 0.9×	51 0.2×	56 0.5×	56 0.8×	24 0.5×	12	389
Puneet Rawat India	13	331 1.0×	152 0.7×	65 0.6×	46 0.6×	21 0.5×	27	424
Pemra Özbek Türkiye	10	281 0.9×	40 0.2×	56 0.5×	56 0.8×	36 0.8×	39	419
Ursula Kahler Austria	11	228 0.7×	76 0.3×	34 0.3×	23 0.3×	53 1.2×	13	297
Nels Thorsteinson United States	9	245 0.7×	205 0.9×	42 0.4×	66 0.9×	18 0.4×	12	325

Countries citing papers authored by Fergus Boyles

Since Specialization

Citations

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

Fields of papers citing papers by Fergus Boyles

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Fergus Boyles

This figure shows the co-authorship network connecting the top 25 collaborators of Fergus Boyles. A scholar is included among the top collaborators of Fergus Boyles 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 Fergus Boyles. Fergus Boyles 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:

7 of 7 papers shown

1.

DURANT, G. J., Fergus Boyles, Kristian Birchall, Brian D. Marsden, & Charlotte M. Deane. (2025). Robustly interrogating machine learning-based scoring functions: what are they learning?. Bioinformatics. 41(2). 8 indexed citations

2.

Gordon, G., et al.. (2024). PLAbDab-nano: a database of camelid and shark nanobodies from patents and literature. Nucleic Acids Research. 53(D1). D535–D542. 3 indexed citations

3.

DURANT, G. J., Fergus Boyles, Kristian Birchall, & Charlotte M. Deane. (2024). The future of machine learning for small-molecule drug discovery will be driven by data. Nature Computational Science. 4(10). 735–743. 12 indexed citations

4.

Abanades, Brennan, Wing Ki Wong, Fergus Boyles, et al.. (2023). ImmuneBuilder: Deep-Learning models for predicting the structures of immune proteins. Communications Biology. 6(1). 575–575. 150 indexed citations breakdown →

5.

Boyles, Fergus, Charlotte M. Deane, & Garrett M. Morris. (2021). Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained on Docked Poses. Journal of Chemical Information and Modeling. 62(22). 5329–5341. 14 indexed citations

6.

Olsen, Tobias Hegelund, Fergus Boyles, & Charlotte M. Deane. (2021). Observed Antibody Space: A diverse database of cleaned, annotated, and translated unpaired and paired antibody sequences. Protein Science. 31(1). 141–146. 158 indexed citations

7.

Boyles, Fergus, Charlotte M. Deane, & Garrett M. Morris. (2019). Learning from the ligand: using ligand-based features to improve binding affinity prediction. Bioinformatics. 36(3). 758–764. 81 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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