Takako Takeda

1.0k total citations
24 papers, 843 citations indexed

About

Takako Takeda is a scholar working on Molecular Biology, Physiology and Biomaterials. According to data from OpenAlex, Takako Takeda has authored 24 papers receiving a total of 843 indexed citations (citations by other indexed papers that have themselves been cited), including 20 papers in Molecular Biology, 12 papers in Physiology and 6 papers in Biomaterials. Recurrent topics in Takako Takeda's work include Protein Structure and Dynamics (18 papers), Alzheimer's disease research and treatments (12 papers) and Supramolecular Self-Assembly in Materials (6 papers). Takako Takeda is often cited by papers focused on Protein Structure and Dynamics (18 papers), Alzheimer's disease research and treatments (12 papers) and Supramolecular Self-Assembly in Materials (6 papers). Takako Takeda collaborates with scholars based in United States, Spain and Japan. Takako Takeda's co-authors include Dmitri K. Klimov, Hironobu Naiki, Keiichi Higuchi, K Nakakuki, E. Prabhu Raman, Stephen H. Bryant, Ming Hao, Tiejun Cheng, Yanli Wang and Wenling E. Chang and has published in prestigious journals such as The Journal of Chemical Physics, ACS Nano and Bioinformatics.

In The Last Decade

Takako Takeda

23 papers receiving 833 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Takako Takeda United States 16 616 480 302 140 89 24 843
Workalemahu M. Berhanu United States 17 434 0.7× 415 0.9× 134 0.4× 119 0.8× 85 1.0× 23 656
Ali Reza A. Ladiwala United States 8 634 1.0× 723 1.5× 148 0.5× 132 0.9× 53 0.6× 9 1.1k
Ann Tiiman Sweden 13 375 0.6× 592 1.2× 136 0.5× 100 0.7× 56 0.6× 24 795
Eric Pang United States 12 488 0.8× 346 0.7× 92 0.3× 90 0.6× 33 0.4× 19 826
Isao Kaneko Japan 15 614 1.0× 395 0.8× 149 0.5× 35 0.3× 41 0.5× 26 1.2k
Darryl Aucoin United States 11 856 1.4× 1.2k 2.5× 253 0.8× 310 2.2× 120 1.3× 11 1.5k
Peter Hunt United Kingdom 18 519 0.8× 227 0.5× 273 0.9× 25 0.2× 76 0.9× 42 1.0k
Jung-Suk Choi United States 9 211 0.3× 383 0.8× 151 0.5× 33 0.2× 46 0.5× 10 651
Yves Jacquot France 22 711 1.2× 177 0.4× 158 0.5× 25 0.2× 50 0.6× 61 1.3k
Eleri Hughes United Kingdom 16 524 0.9× 265 0.6× 50 0.2× 70 0.5× 99 1.1× 33 761

Countries citing papers authored by Takako Takeda

Since Specialization
Citations

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

Fields of papers citing papers by Takako Takeda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Takako Takeda

This figure shows the co-authorship network connecting the top 25 collaborators of Takako Takeda. A scholar is included among the top collaborators of Takako Takeda 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 Takako Takeda. Takako Takeda 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.
Novák, Richard, Sahil Loomba, Anish Vasan, et al.. (2025). AI-enabled drug prediction and gene network analysis reveal therapeutic use of vorinostat for Rett Syndrome in preclinical models. Communications Medicine. 5(1). 249–249.
2.
Sheehan, Katherine M., Haleh Fotowat, Michael Lewandowski, et al.. (2024). Donepezil Nanoemulsion Induces a Torpor-like State with Reduced Toxicity in Nonhibernating Xenopus laevis Tadpoles. ACS Nano. 18(35). 23991–24003. 3 indexed citations
3.
Takeda, Takako, Ming Hao, Tiejun Cheng, Stephen H. Bryant, & Yanli Wang. (2017). Predicting drug–drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge. Journal of Cheminformatics. 9(1). 16–16. 96 indexed citations
4.
Cheng, Tiejun, Ming Hao, Takako Takeda, Stephen H. Bryant, & Yanli Wang. (2017). Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review. The AAPS Journal. 19(5). 1264–1275. 37 indexed citations
5.
Takeda, Takako, Yanli Wang, & Stephen H. Bryant. (2016). Structural insights of a PI3K/mTOR dual inhibitor with the morpholino-triazine scaffold. Journal of Computer-Aided Molecular Design. 30(4). 323–330. 12 indexed citations
6.
Hong, Bo, et al.. (2012). High performance transcription factor-DNA docking with GPU computing. Proteome Science. 10(S1). S17–S17. 17 indexed citations
7.
Takeda, Takako, Rosario I. Corona, & Jun‐tao Guo. (2012). A knowledge-based orientation potential for transcription factor-DNA docking. Bioinformatics. 29(3). 322–330. 11 indexed citations
8.
Takeda, Takako & Dmitri K. Klimov. (2010). Side Chain Interactions can Impede Amyloid Fibril Growth:Replica Exchange Simulations of Abeta Peptide Mutant. Biophysical Journal. 98(3). 649a–649a. 4 indexed citations
9.
Takeda, Takako, et al.. (2010). Mapping Conformational Ensembles of Aβ Oligomers in Molecular Dynamics Simulations. Biophysical Journal. 99(6). 1949–1958. 41 indexed citations
10.
Takeda, Takako, Wenling E. Chang, E. Prabhu Raman, & Dmitri K. Klimov. (2010). Binding of nonsteroidal anti‐inflammatory drugs to Aβ fibril. Proteins Structure Function and Bioinformatics. 78(13). 2849–2860. 29 indexed citations
11.
Chang, Wenling E., Takako Takeda, E. Prabhu Raman, & Dmitri K. Klimov. (2010). Molecular Dynamics Simulations of Anti-Aggregation Effect of Ibuprofen. Biophysical Journal. 98(11). 2662–2670. 22 indexed citations
12.
Takeda, Takako, et al.. (2010). Globular state in the oligomers formed by Aβ peptides. The Journal of Chemical Physics. 132(22). 225101–225101. 9 indexed citations
13.
Takeda, Takako & Dmitri K. Klimov. (2010). Computational Backbone Mutagenesis of Aβ Peptides: Probing the Role of Backbone Hydrogen Bonds in Aggregation. The Journal of Physical Chemistry B. 114(14). 4755–4762. 10 indexed citations
14.
Takeda, Takako & Dmitri K. Klimov. (2009). Interpeptide interactions induce helix to strand structural transition in Aβ peptides. Proteins Structure Function and Bioinformatics. 77(1). 1–13. 51 indexed citations
15.
Takeda, Takako & Dmitri K. Klimov. (2009). Replica Exchange Simulations of the Thermodynamics of Aβ Fibril Growth. Biophysical Journal. 96(2). 442–452. 92 indexed citations
16.
Takeda, Takako & Dmitri K. Klimov. (2009). Probing Energetics of Aβ Fibril Elongation by Molecular Dynamics Simulations. Biophysical Journal. 96(11). 4428–4437. 45 indexed citations
17.
Raman, E. Prabhu, Takako Takeda, & Dmitri K. Klimov. (2009). Molecular Dynamics Simulations of Ibuprofen Binding to Aβ Peptides. Biophysical Journal. 97(7). 2070–2079. 51 indexed citations
18.
Takeda, Takako & Dmitri K. Klimov. (2008). Temperature-Induced Dissociation of Aβ Monomers from Amyloid Fibril. Biophysical Journal. 95(4). 1758–1772. 22 indexed citations
19.
Raman, E. Prabhu, Takako Takeda, Valeri Barsegov, & Dmitri K. Klimov. (2007). Mechanical Unbinding of Aβ Peptides from Amyloid Fibrils. Journal of Molecular Biology. 373(3). 785–800. 14 indexed citations
20.
Takeda, Takako & Dmitri K. Klimov. (2007). Dissociation of Aβ16–22 Amyloid Fibrils Probed by Molecular Dynamics. Journal of Molecular Biology. 368(4). 1202–1213. 26 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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