Shintaro Nakayama

823 total citations
21 papers, 589 citations indexed

About

Shintaro Nakayama is a scholar working on Molecular Biology, Pharmacology and Nuclear and High Energy Physics. According to data from OpenAlex, Shintaro Nakayama has authored 21 papers receiving a total of 589 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Molecular Biology, 4 papers in Pharmacology and 4 papers in Nuclear and High Energy Physics. Recurrent topics in Shintaro Nakayama's work include Drug-Induced Hepatotoxicity and Protection (4 papers), Nuclear physics research studies (4 papers) and Computational Drug Discovery Methods (3 papers). Shintaro Nakayama is often cited by papers focused on Drug-Induced Hepatotoxicity and Protection (4 papers), Nuclear physics research studies (4 papers) and Computational Drug Discovery Methods (3 papers). Shintaro Nakayama collaborates with scholars based in Japan, United States and Netherlands. Shintaro Nakayama's co-authors include Toshiharu Horie, Yasuhiro Masubuchi, Hideo Takakusa, Osamu Okazaki, Atsushi Kurihara, Daisuke Nakai, Ryo Atsumi, Yoshimasa Kobayashi, Yoko Nagai and Hiroshi Masumoto and has published in prestigious journals such as Science, Journal of Molecular Biology and Analytical Chemistry.

In The Last Decade

Shintaro Nakayama

20 papers receiving 561 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Shintaro Nakayama Japan 10 274 230 122 70 64 21 589
Frank W. Lee United States 13 218 0.8× 173 0.8× 184 1.5× 82 1.2× 74 1.2× 21 698
Sean R. Marcsisin United States 16 162 0.6× 222 1.0× 199 1.6× 51 0.7× 66 1.0× 21 799
Debra Luffer‐Atlas United States 14 150 0.5× 176 0.8× 86 0.7× 51 0.7× 38 0.6× 23 490
Ragini Vuppugalla United States 16 458 1.7× 214 0.9× 304 2.5× 150 2.1× 90 1.4× 27 898
Josh T. Pearson United States 16 204 0.7× 205 0.9× 176 1.4× 76 1.1× 37 0.6× 23 633
Aaron M. Moss United States 7 117 0.4× 114 0.5× 193 1.6× 47 0.7× 34 0.5× 10 453
Rhys D.O. Jones United Kingdom 14 400 1.5× 264 1.1× 306 2.5× 243 3.5× 62 1.0× 32 925
Peter J. H. Webborn United Kingdom 17 316 1.2× 321 1.4× 354 2.9× 107 1.5× 36 0.6× 23 1.0k
Zeqi Huang China 13 221 0.8× 362 1.6× 213 1.7× 20 0.3× 45 0.7× 36 804
B. Kevin Park United Kingdom 13 157 0.6× 151 0.7× 93 0.8× 27 0.4× 61 1.0× 15 485

Countries citing papers authored by Shintaro Nakayama

Since Specialization
Citations

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

Fields of papers citing papers by Shintaro Nakayama

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shintaro Nakayama

This figure shows the co-authorship network connecting the top 25 collaborators of Shintaro Nakayama. A scholar is included among the top collaborators of Shintaro Nakayama 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 Shintaro Nakayama. Shintaro Nakayama 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.
Vaddady, Pavan, et al.. (2024). Concentration‐QTcF analysis of quizartinib in patients with newly diagnosed FLT3‐internal‐tandem‐duplication‐positive acute myeloid leukemia. Clinical and Translational Science. 17(11). e70065–e70065. 1 indexed citations
3.
Goto, Kōichi, Yoshimasa Shiraishi, Haruyasu Murakami, et al.. (2023). Phase 1 study of DS‐1205c combined with gefitinib for EGFR mutation‐positive non‐small cell lung cancer. Cancer Medicine. 12(6). 7090–7104. 6 indexed citations
4.
Furuta, Akane, Shintaro Nakayama, Maki Yoshio, et al.. (2022). Programmable molecular transport achieved by engineering protein motors to move on DNA nanotubes. Science. 375(6585). 1159–1164. 51 indexed citations
5.
Tanaka, Yoshiki, Shintaro Nakayama, Shin‐ichiro Narita, et al.. (2018). Structural Basis for the Function of the β-Barrel Assembly-Enhancing Protease BepA. Journal of Molecular Biology. 431(3). 625–635. 9 indexed citations
6.
Furukawa, Arata, et al.. (2018). Remote Coupled Drastic β-Barrel to β-Sheet Transition of the Protein Translocation Motor. Structure. 26(3). 485–489.e2. 11 indexed citations
7.
Yamagata, T., et al.. (2017). Effect of the nuclear medium onα-cluster excitation inLi6. Physical review. C. 95(4). 8 indexed citations
8.
Westerhoff, Hans V., et al.. (2015). Systems Pharmacology: An opinion on how to turn the impossible into grand challenges. Drug Discovery Today Technologies. 15. 23–31. 15 indexed citations
9.
Nakayama, Shintaro, et al.. (2013). Simple Screening Method for Radioactive Concentration Using a Portable Dose Rate Meter. 12(2). 56–60. 1 indexed citations
11.
Oitate, Masataka, Shintaro Nakayama, Takashi Ito, et al.. (2011). Prediction of Human Plasma Concentration-time Profiles of Monoclonal Antibodies from Monkey Data by a Species-invariant Time Method. Drug Metabolism and Pharmacokinetics. 27(3). 354–359. 27 indexed citations
12.
Nakayama, Shintaro, Hideo Takakusa, Akiko Watanabe, et al.. (2011). Combination of GSH Trapping and Time-Dependent Inhibition Assays as a Predictive Method of Drugs Generating Highly Reactive Metabolites. Drug Metabolism and Disposition. 39(7). 1247–1254. 25 indexed citations
13.
Nakayama, Shintaro, Ryo Atsumi, Hideo Takakusa, et al.. (2010). A ZONE CLASSIFICATION SYSTEM FOR RISK ASSESSMENT OF IDIOSYNCRATIC DRUG TOXICITY USING DAILY DOSE AND COVALENT BINDING. Medical Entomology and Zoology. 25. 31–31. 12 indexed citations
14.
Miyoshi, Hirokazu, et al.. (2010). Preparation of 3H-ATP-incorporated silica nanoparticles and its diffusive release. Journal of Non-Crystalline Solids. 356(50-51). 2889–2895. 2 indexed citations
15.
Nakayama, Shintaro, Ryo Atsumi, Hideo Takakusa, et al.. (2009). A Zone Classification System for Risk Assessment of Idiosyncratic Drug Toxicity Using Daily Dose and Covalent Binding. Drug Metabolism and Disposition. 37(9). 1970–1977. 175 indexed citations
16.
Takakusa, Hideo, et al.. (2008). Covalent Binding and Tissue Distribution/Retention Assessment of Drugs Associated with Idiosyncratic Drug Toxicity. Drug Metabolism and Disposition. 36(9). 1770–1779. 56 indexed citations
17.
Masubuchi, Yasuhiro, Shintaro Nakayama, & Toshiharu Horie. (2002). Role of mitochondrial permeability transition in diclofenac-induced hepatocyte injury in rats. Hepatology. 35(3). 544–551. 150 indexed citations
18.
Nakayama, Shintaro, T. Yamagata, H. Akimune, et al.. (2002). Cluster-Excitations in6Li and7Li. Progress of Theoretical Physics Supplement. 146. 603–604. 7 indexed citations
19.
Nakayama, Shintaro, et al.. (1983). Effective Coupling Constants for Spin-Flip and Non Spin-Flip E1 Transitions in A∼90 Nuclei. Journal of the Physical Society of Japan. 52(7). 2325–2331. 2 indexed citations
20.
Nakayama, Shintaro, et al.. (1979). Gamma Deexcitation Mechanism of Rare-Earth Compound Nuclei Produced by (3He, xn) and (α, xn) Reactions. Journal of the Physical Society of Japan. 46(1). 6–12. 7 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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