Daisuke Kihara

12.3k total citations
233 papers, 5.9k citations indexed

About

Daisuke Kihara is a scholar working on Molecular Biology, Materials Chemistry and Computational Theory and Mathematics. According to data from OpenAlex, Daisuke Kihara has authored 233 papers receiving a total of 5.9k indexed citations (citations by other indexed papers that have themselves been cited), including 206 papers in Molecular Biology, 88 papers in Materials Chemistry and 44 papers in Computational Theory and Mathematics. Recurrent topics in Daisuke Kihara's work include Protein Structure and Dynamics (124 papers), Enzyme Structure and Function (81 papers) and Machine Learning in Bioinformatics (56 papers). Daisuke Kihara is often cited by papers focused on Protein Structure and Dynamics (124 papers), Enzyme Structure and Function (81 papers) and Machine Learning in Bioinformatics (56 papers). Daisuke Kihara collaborates with scholars based in United States, Japan and South Korea. Daisuke Kihara's co-authors include Jeffrey Skolnick, Lee Sael, Genki Terashi, Troy Hawkins, Juan Esquivel‐Rodríguez, Charles Christoffer, Woong‐Hee Shin, Yi‐Feng Yang, Yang Zhang and Vishwesh Venkatraman and has published in prestigious journals such as Nature, Science and Proceedings of the National Academy of Sciences.

In The Last Decade

Daisuke Kihara

227 papers receiving 5.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daisuke Kihara United States 45 4.7k 1.7k 1.1k 424 359 233 5.9k
Jianlin Cheng United States 52 7.4k 1.6× 1.9k 1.1× 1.2k 1.1× 205 0.5× 362 1.0× 259 9.9k
Willy Wriggers United States 37 4.6k 1.0× 1.8k 1.1× 249 0.2× 973 2.3× 467 1.3× 95 6.5k
Fei Long United Kingdom 19 8.4k 1.8× 3.0k 1.8× 267 0.2× 376 0.9× 251 0.7× 40 11.6k
John Jumper United States 15 4.1k 0.9× 1.3k 0.7× 609 0.6× 49 0.1× 415 1.2× 24 5.7k
Alexey G. Murzin United Kingdom 51 14.7k 3.1× 4.0k 2.4× 938 0.9× 148 0.3× 710 2.0× 103 18.6k
Jianyang Zeng China 28 2.8k 0.6× 469 0.3× 1.4k 1.3× 90 0.2× 56 0.2× 78 3.5k
Peter L. Freddolino United States 41 3.9k 0.8× 973 0.6× 326 0.3× 76 0.2× 329 0.9× 83 5.5k
Haim J. Wolfson Israel 61 11.7k 2.5× 3.7k 2.2× 3.3k 3.1× 296 0.7× 704 2.0× 176 16.5k
Jinbo Xu United States 41 6.3k 1.3× 1.3k 0.8× 1.1k 1.0× 28 0.1× 173 0.5× 153 7.9k
Pablo Chacón Spain 35 4.1k 0.9× 1.1k 0.7× 309 0.3× 411 1.0× 229 0.6× 72 5.3k

Countries citing papers authored by Daisuke Kihara

Since Specialization
Citations

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

Fields of papers citing papers by Daisuke Kihara

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Daisuke Kihara

This figure shows the co-authorship network connecting the top 25 collaborators of Daisuke Kihara. A scholar is included among the top collaborators of Daisuke Kihara 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 Daisuke Kihara. Daisuke Kihara 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.
Bekker, Gert‐Jan, Chioko Nagao, Matsuyuki Shirota, et al.. (2025). Protein Data Bank Japan: Computational Resources for Analysis of Protein Structures. Journal of Molecular Biology. 437(15). 169013–169013. 5 indexed citations
2.
Terashi, Genki, Roland Riek, Jason Greenwald, et al.. (2025). Structural Insights and Functional Dynamics of β-Lactoglobulin Fibrils. Nano Letters. 25(45). 16146–16153.
3.
Mukherjee, Somnath, Martin Gustavsson, James R. Fuller, et al.. (2025). Effect of phosphorylation barcodes on arrestin binding to a chemokine receptor. Nature. 643(8070). 280–287. 3 indexed citations
4.
Wang, Xiao, et al.. (2024). DiffModeler: large macromolecular structure modeling for cryo-EM maps using a diffusion model. Nature Methods. 21(12). 2307–2317. 18 indexed citations
5.
Bou‐Abdallah, Fadi, et al.. (2024). Unveiling the stochastic nature of human heteropolymer ferritin self‐assembly mechanism. Protein Science. 33(8). e5104–e5104. 9 indexed citations
6.
Terashi, Genki, et al.. (2023). Enhancing cryo-EM maps with 3D deep generative networks for assisting protein structure modeling. Bioinformatics. 39(8). 5–7. 7 indexed citations
7.
Wang, Xiao, Genki Terashi, & Daisuke Kihara. (2023). CryoREAD: De novo structure modeling for nucleic acids in cryo-EM maps using deep learning. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations
8.
Wang, Xiao, Genki Terashi, & Daisuke Kihara. (2023). CryoREAD: de novo structure modeling for nucleic acids in cryo-EM maps using deep learning. Nature Methods. 20(11). 1739–1747. 22 indexed citations
9.
Wang, Xiao, Haoqi Fan, Yuandong Tian, Daisuke Kihara, & Xinlei Chen. (2022). On the Importance of Asymmetry for Siamese Representation Learning. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 16549–16558. 23 indexed citations
10.
Terashi, Genki, Xiao Wang, & Daisuke Kihara. (2022). Protein model refinement for cryo-EM maps using AlphaFold2 and the DAQ score. Acta Crystallographica Section D Structural Biology. 79(1). 10–21. 9 indexed citations
11.
Terashi, Genki, et al.. (2022). Residue-wise local quality estimation for protein models from cryo-EM maps. Nature Methods. 19(9). 1116–1125. 26 indexed citations
12.
Biasotti, Silvia, Walter Rocchia, Hao Huang, et al.. (2022). SHREC 2022: Protein–ligand binding site recognition. Computers & Graphics. 107. 20–31. 12 indexed citations
13.
Rao, R. Shyama Prasad, Nagib Ahsan, Chunhui Xu, et al.. (2021). Evolutionary Dynamics of Indels in SARS-CoV-2 Spike Glycoprotein. Evolutionary Bioinformatics. 17. 3243625768–3243625768. 6 indexed citations
14.
Ramadesikan, Swetha, Jennifer Lee, Kayalvizhi Madhivanan, et al.. (2021). Genotype & phenotype in Lowe Syndrome: specific OCRL1 patient mutations differentially impact cellular phenotypes. Human Molecular Genetics. 30(3-4). 198–212. 13 indexed citations
15.
Wang, Xiao, et al.. (2021). Detecting protein and DNA/RNA structures in cryo-EM maps of intermediate resolution using deep learning. Nature Communications. 12(1). 2302–2302. 29 indexed citations
16.
Shin, Woong‐Hee, et al.. (2020). <p>Current Challenges and Opportunities in Designing Protein–Protein Interaction Targeted Drugs</p>. PubMed. Volume 13. 11–25. 45 indexed citations
17.
Terashi, Genki & Daisuke Kihara. (2018). De novo main-chain modeling for EM maps using MAINMAST. Nature Communications. 9(1). 1618–1618. 78 indexed citations
18.
Liu, Yue, Ju Sheng, Andrei Fokine, et al.. (2015). Structure and inhibition of EV-D68, a virus that causes respiratory illness in children. Science. 347(6217). 71–74. 142 indexed citations
19.
Hawkins, Troy, Meghana Chitale, & Daisuke Kihara. (2010). Functional enrichment analyses and construction of functional similarity networks with high confidence function prediction by PFP. BMC Bioinformatics. 11(1). 265–265. 15 indexed citations
20.
Hawkins, Troy, Meghana Chitale, & Daisuke Kihara. (2008). New paradigm in protein function prediction for large scale omics analysis. Molecular BioSystems. 4(3). 223–231. 23 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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Jianlin Cheng Breakdown of academic impact, for papers by Willy Wriggers Breakdown of academic impact, for papers by Fei Long Breakdown of academic impact, for papers by John Jumper Breakdown of academic impact, for papers by Alexey G. Murzin Breakdown of academic impact, for papers by Jianyang Zeng Breakdown of academic impact, for papers by Peter L. Freddolino Breakdown of academic impact, for papers by Haim J. Wolfson Breakdown of academic impact, for papers by Jinbo Xu Breakdown of academic impact, for papers by Pablo Chacón
Rankless by CCL
2026