Ryo Kunimoto

931 total citations
27 papers, 757 citations indexed

About

Ryo Kunimoto is a scholar working on Molecular Biology, Computational Theory and Mathematics and Pharmacology. According to data from OpenAlex, Ryo Kunimoto has authored 27 papers receiving a total of 757 indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 17 papers in Computational Theory and Mathematics and 5 papers in Pharmacology. Recurrent topics in Ryo Kunimoto's work include Computational Drug Discovery Methods (17 papers), Bioinformatics and Genomic Networks (5 papers) and Analytical Chemistry and Chromatography (4 papers). Ryo Kunimoto is often cited by papers focused on Computational Drug Discovery Methods (17 papers), Bioinformatics and Genomic Networks (5 papers) and Analytical Chemistry and Chromatography (4 papers). Ryo Kunimoto collaborates with scholars based in Germany, Japan and France. Ryo Kunimoto's co-authors include Gozoh Tsujimoto, Jürgen Bajorath, Ken Kawamoto, Kazuya Kawahara, Masayuki Nakagawa, Hideki Enokida, Kazuki Toki, Takeshi Chiyomaru, Yasushi Okuno and Naohiko Seki and has published in prestigious journals such as Nucleic Acids Research, Journal of Medicinal Chemistry and International Journal of Cancer.

In The Last Decade

Ryo Kunimoto

25 papers receiving 744 citations

Peers

Ryo Kunimoto

CTM

SURGE

PHARM

Thomas M. Kollmeyer United States

Sukjoon Yoon South Korea

Jorge Z. Torres United States

Baofeng Lian China

Ana Igea Spain

Mengzhu Xue China

Vasileios Stathias United States

R.C. Hillig Germany

Vinothini Rajeeve United Kingdom

Nada Lallous Canada

Thomas M. Kollmeyer United States

Ryo Kunimoto

498 ×0.8

389 ×0.9

110 ×0.7

CTM

42 ×0.8

SURGE

107 ×2.2

PHARM

Citations per year, relative to Ryo Kunimoto Ryo Kunimoto (= 1×) peers Thomas M. Kollmeyer

Countries citing papers authored by Ryo Kunimoto

Since Specialization

Citations

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

Fields of papers citing papers by Ryo Kunimoto

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ryo Kunimoto

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

All Works

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20 of 20 papers shown

Tanaka, Yoshihisa, et al.. (2025). Long-read RNA sequencing unveils a novel cryptic exon in MNAT1 along with its full-length transcript structure in TDP-43 proteinopathy. Communications Biology. 8(1). 1056–1056.

Taniguchi, Junichi, Haruyuki Takaki, Ryo Kunimoto, et al.. (2022). Use of Microballoon Catheter in Transarterial Ethanol Embolization of Renal Angiomyolipoma: A Retrospective Comparative Study with Historical Control?. PubMed. 7(1). 9–16. 1 indexed citations

Takaki, Haruyuki, Kaoru Kobayashi, Hiroshi Kodama, et al.. (2022). Clinical Experience of Percutaneous Radiofrequency Ablation Using an arfa RF ABLATION SYSTEM<sup>®</sup> in Various Organs. PubMed. 7(3). 93–99.

Moriwaki, Hirotomo, et al.. (2022). Global Analysis of Deep Learning Prediction Using Large-Scale In-House Kinome-Wide Profiling Data. ACS Omega. 7(22). 18374–18381. 4 indexed citations

Kitajima, Kazuhiro, Ryo Kunimoto, Hiroyuki Yokoyama, et al.. (2021). CT Findings of Primary Renal Angiosarcoma. Case Reports in Oncology. 14(1). 212–216. 4 indexed citations

Kunimoto, Ryo, et al.. (2021). Systematic mapping of R-group space enables the generation of an R-group replacement system for medicinal chemistry. European Journal of Medicinal Chemistry. 225. 113771–113771. 5 indexed citations

Kunimoto, Ryo & Jürgen Bajorath. (2018). Design of a tripartite network for the prediction of drug targets. Journal of Computer-Aided Molecular Design. 32(2). 321–330. 7 indexed citations

Vogt, Martin, et al.. (2018). Computational Assessment of Chemical Saturation of Analogue Series under Varying Conditions. ACS Omega. 3(11). 15799–15808. 7 indexed citations

Kunimoto, Ryo, et al.. (2017). From bird’s eye views to molecular communities: two-layered visualization of structure–activity relationships in large compound data sets. Journal of Computer-Aided Molecular Design. 31(11). 961–977. 6 indexed citations

10.

Miljković, Filip, Ryo Kunimoto, & Jürgen Bajorath. (2017). Identifying Relationships Between Unrelated Pharmaceutical Target Proteins On the Basis of Shared Active Compounds. Future Science OA. 3(3). FSO212–FSO212. 3 indexed citations

11.

Kunimoto, Ryo & Jürgen Bajorath. (2017). Exploring sets of molecules from patents and relationships to other active compounds in chemical space networks. Journal of Computer-Aided Molecular Design. 31(9). 779–788. 8 indexed citations

12.

Kunimoto, Ryo, Martin Vogt, & Jürgen Bajorath. (2016). Maximum common substructure-based Tversky index: an asymmetric hybrid similarity measure. Journal of Computer-Aided Molecular Design. 30(7). 523–531. 16 indexed citations

13.

Kunimoto, Ryo, Martin Vogt, & Jürgen Bajorath. (2016). Tracing compound pathways using chemical space networks. MedChemComm. 8(2). 376–384. 4 indexed citations

14.

Hu, Ye, Ryo Kunimoto, & Jürgen Bajorath. (2016). Mapping of inhibitors and activity data to the human kinome and exploring promiscuity from a ligand and target perspective. Chemical Biology & Drug Design. 89(6). 834–845. 14 indexed citations

15.

Takahashi, Jun, Ichiro Hijikuro, Takeshi Kihara, et al.. (2010). Design, synthesis, evaluation and QSAR analysis of N1-substituted norcymserine derivatives as selective butyrylcholinesterase inhibitors. Bioorganic & Medicinal Chemistry Letters. 20(5). 1718–1720. 14 indexed citations

16.

Feng, Chunlai, Michihiro Araki, Ryo Kunimoto, et al.. (2009). GEM-TREND: a web tool for gene expression data mining toward relevant network discovery. BMC Genomics. 10(1). 411–411. 23 indexed citations

17.

Enokida, Hideki, Yasushi Okuno, Ryo Kunimoto, et al.. (2009). Identification of novel microRNA targets based on microRNA signatures in bladder cancer. International Journal of Cancer. 125(2). 345–352. 349 indexed citations

18.

Tsuchiya, Soken, Ryo Kunimoto, Yasushi Okuno, et al.. (2009). MicroRNA-338-3p and microRNA-451 contribute to the formation of basolateral polarity in epithelial cells. Nucleic Acids Research. 37(11). 3821–3827. 56 indexed citations

19.

Ruike, Yoshinao, Atsuhiko Ichimura, Soken Tsuchiya, et al.. (2008). Global correlation analysis for micro-RNA and mRNA expression profiles in human cell lines. Journal of Human Genetics. 53(6). 515–523. 76 indexed citations

20.

Okuno, Yusuke, Akiko Tamon, Hiroaki Yabuuchi, et al.. (2007). GLIDA: GPCR ligand database for chemical genomics drug discovery database and tools update. Nucleic Acids Research. 36(Database). D907–D912. 108 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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