Kei Terayama

2.1k total citations
91 papers, 1.4k citations indexed

About

Kei Terayama is a scholar working on Materials Chemistry, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Kei Terayama has authored 91 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 42 papers in Materials Chemistry, 27 papers in Computational Theory and Mathematics and 24 papers in Molecular Biology. Recurrent topics in Kei Terayama's work include Machine Learning in Materials Science (37 papers), Computational Drug Discovery Methods (25 papers) and Protein Structure and Dynamics (13 papers). Kei Terayama is often cited by papers focused on Machine Learning in Materials Science (37 papers), Computational Drug Discovery Methods (25 papers) and Protein Structure and Dynamics (13 papers). Kei Terayama collaborates with scholars based in Japan, United Kingdom and France. Kei Terayama's co-authors include Koji Tsuda, Ryo Tamura, Yasushi Okuno, Kazuki Yoshizoe, Masato Sumita, Shoichi Ishida, Jinzhe Zhang, Xiufeng Yang, Kiyosei Takasu and Katsunori Mizuno and has published in prestigious journals such as Nature Communications, The Journal of Chemical Physics and SHILAP Revista de lepidopterología.

In The Last Decade

Kei Terayama

87 papers receiving 1.4k citations

Peers

Kei Terayama
Tristan Bereau Germany
Zhitao Zhang China
Garrett B. Goh United States
Sungwoo Park South Korea
Rudiyanto Gunawan United States
Christoph Kreisbeck United States
Nathan O. Hodas United States
Yixin Cao China
Tristan Bereau Germany
Kei Terayama
Citations per year, relative to Kei Terayama Kei Terayama (= 1×) peers Tristan Bereau

Countries citing papers authored by Kei Terayama

Since Specialization
Citations

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

Fields of papers citing papers by Kei Terayama

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Kei Terayama

This figure shows the co-authorship network connecting the top 25 collaborators of Kei Terayama. A scholar is included among the top collaborators of Kei Terayama 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 Kei Terayama. Kei Terayama 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.
Ishida, Shoichi, Tomohiro Sato, Teruki Honma, & Kei Terayama. (2025). Large language models open new way of AI-assisted molecule design for chemists. Journal of Cheminformatics. 17(1). 36–36. 5 indexed citations
2.
Wang, Shulei, Katsunori Mizuno, Shigeru Tabeta, et al.. (2023). An efficient segmentation method based on semi-supervised learning for seafloor monitoring in Pujada Bay, Philippines. Ecological Informatics. 78. 102371–102371. 5 indexed citations
3.
Terayama, Kei, et al.. (2022). Correction to: Semi-automation of gesture annotation by machine learning and human collaboration. Language Resources and Evaluation. 56(3). 701–701. 1 indexed citations
4.
Fujita, T., Kei Terayama, Masato Sumita, et al.. (2022). Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring. Science and Technology of Advanced Materials. 23(1). 352–360. 8 indexed citations
5.
Sumita, Masato, Kei Terayama, Ryo Tamura, & Koji Tsuda. (2022). QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization. Journal of Chemical Information and Modeling. 62(18). 4427–4434. 6 indexed citations
6.
Ishida, Shoichi, et al.. (2022). Selective Inhibitor Design for Kinase Homologs Using Multiobjective Monte Carlo Tree Search. Journal of Chemical Information and Modeling. 62(22). 5351–5360. 12 indexed citations
7.
Tokuhisa, Atsushi, et al.. (2022). Single-Image Super-Resolution Improvement of X-ray Single-Particle Diffraction Images Using a Convolutional Neural Network. Journal of Chemical Information and Modeling. 62(14). 3352–3364. 5 indexed citations
8.
Chen, Ta‐Te, Ryo Tamura, Kei Terayama, et al.. (2022). Topological alternation from structurally adaptable to mechanically stable crosslinked polymer. Science and Technology of Advanced Materials. 23(1). 66–75. 9 indexed citations
9.
Tamura, Ryo, Kwangsik Han, Taichi Abe, et al.. (2022). Machine-Learning-Based phase diagram construction for high-throughput batch experiments. SHILAP Revista de lepidopterología. 2(1). 153–161. 9 indexed citations
10.
Terayama, Kei, Katsunori Mizuno, Shigeru Tabeta, et al.. (2021). Cost‐effective seafloor habitat mapping using a portable speedy sea scanner and deep‐learning‐based segmentation: A sea trial at Pujada Bay, Philippines. Methods in Ecology and Evolution. 13(2). 339–345. 19 indexed citations
11.
Sun, Xiaolin, Ryo Tamura, Masato Sumita, et al.. (2021). Integrating Incompatible Assay Data Sets with Deep Preference Learning. ACS Medicinal Chemistry Letters. 13(1). 70–75. 2 indexed citations
12.
Ma, Biao, Kei Terayama, Shigeyuki Matsumoto, et al.. (2021). Structure-Based de Novo Molecular Generator Combined with Artificial Intelligence and Docking Simulations. Journal of Chemical Information and Modeling. 61(7). 3304–3313. 44 indexed citations
13.
Terayama, Kei, Masato Sumita, Ryo Tamura, & Koji Tsuda. (2021). Black-Box Optimization for Automated Discovery. Accounts of Chemical Research. 54(6). 1334–1346. 85 indexed citations
14.
Tsuda, Koji, et al.. (2020). Computer Vision-Based Approach for Quantifying Occupational Therapists’ Qualitative Evaluations of Postural Control. Occupational Therapy International. 2020. 1–9. 4 indexed citations
15.
Kanada, Ryo, Atsushi Tokuhisa, Koji Tsuda, Yasushi Okuno, & Kei Terayama. (2020). Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning. Biomolecules. 10(3). 482–482. 9 indexed citations
16.
17.
Terayama, Kei, et al.. (2019). evERdock BAI: Machine-learning-guided selection of protein-protein complex structure. The Journal of Chemical Physics. 151(21). 215104–215104. 8 indexed citations
18.
Saito, Yu, Kei Terayama, Masaru Onga, et al.. (2019). Deep-learning-based quality filtering of mechanically exfoliated 2D crystals. npj Computational Materials. 5(1). 57 indexed citations
19.
Terayama, Kei, Tomoki Yamashita, Tamio Oguchi, & Koji Tsuda. (2018). Fine-grained optimization method for crystal structure prediction. npj Computational Materials. 4(1). 25 indexed citations
20.
Terayama, Kei, Hiroaki Iwata, Mitsugu Araki, Yasushi Okuno, & Koji Tsuda. (2017). Machine learning accelerates MD-based binding pose prediction between ligands and proteins. Bioinformatics. 34(5). 770–778. 19 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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