Toby Passioura

2.0k total citations
50 papers, 1.5k citations indexed

About

Toby Passioura is a scholar working on Molecular Biology, Oncology and Organic Chemistry. According to data from OpenAlex, Toby Passioura has authored 50 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 39 papers in Molecular Biology, 11 papers in Oncology and 7 papers in Organic Chemistry. Recurrent topics in Toby Passioura's work include Chemical Synthesis and Analysis (14 papers), RNA and protein synthesis mechanisms (11 papers) and RNA Interference and Gene Delivery (7 papers). Toby Passioura is often cited by papers focused on Chemical Synthesis and Analysis (14 papers), RNA and protein synthesis mechanisms (11 papers) and RNA Interference and Gene Delivery (7 papers). Toby Passioura collaborates with scholars based in Japan, Australia and United States. Toby Passioura's co-authors include Hiroaki Suga, Takayuki Katoh, Yuki Goto, Kenichiro Ito, Alla Dolnikov, Joseph M. Rogers, Geoff Symonds, Sylvie Shen, Amber Goodchild and Richard J. Payne and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Journal of the American Chemical Society and Nucleic Acids Research.

In The Last Decade

Toby Passioura

48 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Toby Passioura Japan 21 1.2k 303 270 213 125 50 1.5k
Jinzi J. Wu United States 16 777 0.6× 214 0.7× 156 0.6× 175 0.8× 49 0.4× 41 1.2k
Edelmira Cabezas United States 12 1.1k 0.9× 271 0.9× 148 0.5× 114 0.5× 107 0.9× 13 1.7k
L. Nathan Tumey United States 22 702 0.6× 466 1.5× 444 1.6× 503 2.4× 56 0.4× 43 1.4k
Douglas A. Jeffery United States 12 784 0.7× 101 0.3× 260 1.0× 244 1.1× 23 0.2× 15 1.3k
Joakim E. Swedberg Australia 24 1.0k 0.8× 84 0.3× 221 0.8× 196 0.9× 209 1.7× 44 1.3k
Jonathan A. Ellman United States 9 611 0.5× 83 0.3× 190 0.7× 182 0.9× 28 0.2× 9 891
Kristopher Josephson United States 12 843 0.7× 278 0.9× 175 0.6× 224 1.1× 77 0.6× 14 1.3k
Yoshio Aramaki Japan 19 624 0.5× 115 0.4× 393 1.5× 253 1.2× 39 0.3× 22 1.6k
Thomas J. Tolbert United States 21 1.1k 0.9× 429 1.4× 346 1.3× 122 0.6× 22 0.2× 48 1.3k
Jason Phan United States 22 1.6k 1.4× 123 0.4× 227 0.8× 459 2.2× 10 0.1× 34 2.0k

Countries citing papers authored by Toby Passioura

Since Specialization
Citations

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

Fields of papers citing papers by Toby Passioura

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Toby Passioura

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

All Works

20 of 20 papers shown
1.
Ferraz, María Pía, et al.. (2025). Development of selective nanomolar cyclic peptide ligands as GBA1 enzyme stabilisers. RSC Chemical Biology. 6(4). 563–570. 2 indexed citations
2.
Jing, Xiangyi, Joel P. Mackay, & Toby Passioura. (2025). Macrocyclic peptides as a new class of targeted protein degraders. RSC Chemical Biology. 6(3). 326–337. 3 indexed citations
3.
4.
Franck, Charlotte, Karishma Patel, Louise J. Walport, et al.. (2023). Discovery and characterization of cyclic peptides selective for the C-terminal bromodomains of BET family proteins. Structure. 31(8). 912–923.e4. 5 indexed citations
5.
Ullrich, Sven, Rebecca L. Frkic, Anupriya Aggarwal, et al.. (2022). Antiviral cyclic peptides targeting the main protease of SARS-CoV-2. Chemical Science. 13(13). 3826–3836. 45 indexed citations
6.
Ford, Daniel, Jorge Ripoll‐Rozada, Stijn M. Agten, et al.. (2021). Potent Cyclic Peptide Inhibitors of FXIIa Discovered by mRNA Display with Genetic Code Reprogramming. Journal of Medicinal Chemistry. 64(11). 7853–7876. 14 indexed citations
7.
Patel, Karishma, Louise J. Walport, J.L. Walshe, et al.. (2020). Cyclic peptides can engage a single binding pocket through highly divergent modes. Proceedings of the National Academy of Sciences. 117(43). 26728–26738. 32 indexed citations
8.
Matoba, Kyoko, et al.. (2020). Macrocyclic peptides that inhibit Wnt signallingviainteraction with Wnt3a. RSC Chemical Biology. 1(1). 26–34. 7 indexed citations
9.
Sakai, Katsuya, Toby Passioura, Hiroki Sato, et al.. (2019). Macrocyclic peptide-based inhibition and imaging of hepatocyte growth factor. Nature Chemical Biology. 15(6). 598–606. 58 indexed citations
10.
Passioura, Toby, Koichi Watashi, Kento Fukano, et al.. (2018). De Novo Macrocyclic Peptide Inhibitors of Hepatitis B Virus Cellular Entry. Cell chemical biology. 25(7). 906–915.e5. 50 indexed citations
11.
Kawamura, Akane, Martin Münzel, Tatsuya Kojima, et al.. (2017). Highly selective inhibition of histone demethylases by de novo macrocyclic peptides. Nature Communications. 8(1). 14773–14773. 117 indexed citations
12.
Passioura, Toby & Hiroaki Suga. (2014). Reprogramming the genetic code in vitro. Trends in Biochemical Sciences. 39(9). 400–408. 35 indexed citations
13.
Passioura, Toby & Hiroaki Suga. (2013). Flexizymes, Their Evolutionary History and Diverse Utilities. Topics in current chemistry. 344. 331–345. 17 indexed citations
14.
Passioura, Toby, Amber Goodchild, Andrew King, et al.. (2009). Interfering ribonucleic acids that suppress expression of multiple unrelated genes. BMC Biotechnology. 9(1). 57–57. 2 indexed citations
15.
Goodchild, Amber, et al.. (2007). Cytotoxic G-rich oligodeoxynucleotides: putative protein targets and required sequence motif. Nucleic Acids Research. 35(13). 4562–4572. 20 indexed citations
16.
Shen, Sylvie, Toby Passioura, Geoff Symonds, & Alla Dolnikov. (2007). N-ras oncogene–induced gene expression in human hematopoietic progenitor cells: Upregulation of p16INK4a and p21CIP1/WAF1 correlates with myeloid differentiation. Experimental Hematology. 35(6). 908–919. 10 indexed citations
17.
Passioura, Toby, Alla Dolnikov, Sylvie Shen, & Geoff Symonds. (2005). N-Ras –Induced Growth Suppression of Myeloid Cells Is Mediated by IRF-1. Cancer Research. 65(3). 797–804. 28 indexed citations
18.
Passioura, Toby & Geoff Symonds. (2004). Cancer Gene Suppression Strategies: Issues and Potential. Current Issues in Molecular Biology. 6(2). 89–101.
19.
Shen, Sylvie, Alla Dolnikov, Toby Passioura, et al.. (2004). Mutant N-ras preferentially drives human CD34+ hematopoietic progenitor cells into myeloid differentiation and proliferation both in vitro and in the NOD/SCID mouse. Experimental Hematology. 32(9). 852–860. 23 indexed citations
20.
Dolnikov, Alla, Sylvie Shen, Michelle Millington, et al.. (2003). A sensitive dual-fluorescence reporter system enables positive selection of ras suppressors by suppression of ras-induced apoptosis. Cancer Gene Therapy. 10(10). 745–754. 8 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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