Koji Tsuda

13.9k total citations · 1 hit paper
215 papers, 9.0k citations indexed

About

Koji Tsuda is a scholar working on Molecular Biology, Materials Chemistry and Artificial Intelligence. According to data from OpenAlex, Koji Tsuda has authored 215 papers receiving a total of 9.0k indexed citations (citations by other indexed papers that have themselves been cited), including 76 papers in Molecular Biology, 63 papers in Materials Chemistry and 54 papers in Artificial Intelligence. Recurrent topics in Koji Tsuda's work include Machine Learning in Materials Science (58 papers), Computational Drug Discovery Methods (41 papers) and Gene expression and cancer classification (22 papers). Koji Tsuda is often cited by papers focused on Machine Learning in Materials Science (58 papers), Computational Drug Discovery Methods (41 papers) and Gene expression and cancer classification (22 papers). Koji Tsuda collaborates with scholars based in Japan, Germany and United States. Koji Tsuda's co-authors include Bernhard Schölkopf, Gunnar Rätsch, K. Müller, Mika Sirén, Hisashi Kashima, Kei Terayama, Ryo Tamura, Akihiro Inokuchi, Isao Tanaka and Atsuto Seko and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Physical Review Letters and Nucleic Acids Research.

In The Last Decade

Koji Tsuda

205 papers receiving 8.6k citations

Hit Papers

An introduction to kernel-based learning algorithms 2001 2026 2009 2017 2001 500 1000 1.5k 2.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. An introduction to kernel-based learning algorithms
    2001 2.5k citations Koji Tsuda et al. profile →

Peers

Koji Tsuda
Comparison fields: 5 of 198
  • Artificial Intelligence 2.5k
  • Materials Chemistry 2.4k
  • Molecular Biology 1.9k
  • Computer Vision and Pattern Recognition 1.8k
  • Computational Theory and Mathematics 1.3k
Replace Ryan P. Adams with:
Ryan P. Adams United States
Grégoire Montavon Germany
K. Müller Germany
Pushmeet Kohli United Kingdom
James Kirkpatrick United Kingdom
Long Chen China
Zeming Lin China
Tommi Jaakkola United States
Ashish Ghosh India
Ananth Grama United States
Ryan P. Adams United States View profile →
Citations per field, relative to Koji Tsuda
Koji Tsuda · 1×
Citations per year, relative to Koji Tsuda
Koji Tsuda · 1×

Countries citing papers authored by Koji Tsuda

Since Specialization
Citations

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

Fields of papers citing papers by Koji Tsuda

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Koji Tsuda

This figure shows the co-authorship network connecting the top 25 collaborators of Koji Tsuda. A scholar is included among the top collaborators of Koji Tsuda 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 Koji Tsuda. Koji Tsuda 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
# Work Indexed citations
1
An ANI‐2 enabled open‐source protocol to estimate ligand strain after docking
2024 ·Journal of Computational Chemistry ·Francois Berenger, Koji Tsuda
5
2
Automated odor-blending with one-pot Bayesian optimization
2024 ·Digital Discovery ·(unknown), Kosuke Minami, Kota Shiba, Genki Yoshikawa, Koji Tsuda, Ryo Tamura
2
3
3D-Sensitive Encoding of Pharmacophore Features
2023 ·Journal of Chemical Information and Modeling ·Francois Berenger, Koji Tsuda
3
4
Towards understanding structure–property relations in materials with interpretable deep learning
2023 ·npj Computational Materials ·(unknown), (unknown), (unknown), (unknown), (unknown), Truyen Tran, Tran Doan Huan, Hiori Kino, Takashi Miyake, Koji Tsuda, Hieu‐Chi Dam
23
5
Understanding the evolution of a de novo molecule generator via characteristic functional group monitoring
2022 ·Science and Technology of Advanced Materials ·T. Fujita, Kei Terayama, Masato Sumita, Ryo Tamura, Yasuyuki Nakamura, Masanobu Naito, Koji Tsuda
8
6
QCforever: A Quantum Chemistry Wrapper for Everyone to Use in Black-Box Optimization
2022 ·Journal of Chemical Information and Modeling ·Masato Sumita, Kei Terayama, Ryo Tamura, Koji Tsuda
6
7
Continuous black-box optimization with an Ising machine and random subspace coding
2022 ·Physical Review Research ·(unknown), (unknown), Shu Tanaka, Ryo Tamura, Koji Tsuda
20
8
Hybrid algorithm of Bayesian optimization and evolutionary algorithm in crystal structure prediction
2022 ·SHILAP Revista de lepidopterología ·Tomoki Yamashita, Hiori Kino, Koji Tsuda, Takashi Miyake, Tamio Oguchi
4
9
Integrating Incompatible Assay Data Sets with Deep Preference Learning
2021 ·ACS Medicinal Chemistry Letters ·Xiaolin Sun, Ryo Tamura, Masato Sumita, Kenichi Mori, Kei Terayama, Koji Tsuda
2
10
Black-Box Optimization for Automated Discovery
2021 ·Accounts of Chemical Research ·Kei Terayama, Masato Sumita, Ryo Tamura, Koji Tsuda
85
11
Computer Vision-Based Approach for Quantifying Occupational Therapists’ Qualitative Evaluations of Postural Control
2020 ·Occupational Therapy International ·(unknown), (unknown), (unknown), (unknown), (unknown), (unknown), Koji Tsuda, Kei Terayama
4
12
Exploring Successful Parameter Region for Coarse-Grained Simulation of Biomolecules by Bayesian Optimization and Active Learning
2020 ·Biomolecules ·Ryo Kanada, Atsushi Tokuhisa, Koji Tsuda, Yasushi Okuno, Kei Terayama
9
13
Designing metamaterials with quantum annealing and factorization machines
2020 ·Physical Review Research ·(unknown), Jiang Guo, Shenghong Ju, Shu Tanaka, Koji Tsuda, Junichiro Shiomi, Ryo Tamura
114
14
Li-Ion Conductive Li3PO4-Li3BO3-Li2SO4 Mixture: Prevision through Density Functional Molecular Dynamics and Machine Learning
2019 ·Bulletin of the Chemical Society of Japan ·Masato Sumita, Ryo Tamura, Kenji Homma, Chioko Kaneta, Koji Tsuda
13
15
Data-driven analysis of electron relaxation times in PbTe-type thermoelectric materials
2019 ·SHILAP Revista de lepidopterología ·Yukari Katsura, Masaya Kumagai, (unknown), (unknown), (unknown), (unknown), Y. Imai, (unknown), (unknown), (unknown), Koji Tsuda
1
16
evERdock BAI: Machine-learning-guided selection of protein-protein complex structure
2019 ·The Journal of Chemical Physics ·Kei Terayama, (unknown), Koji Tsuda, Kazuhiro Takemura, Akio Kitao
8
17
Deep-learning-based quality filtering of mechanically exfoliated 2D crystals
2019 ·npj Computational Materials ·Yu Saito, (unknown), Kei Terayama, (unknown), Masaru Onga, Yuji Nakagawa, Yuki M. Itahashi, Yoshihiro Iwasa, Makoto Yamada, Koji Tsuda
57
18
Machine-Learning-Guided Mutagenesis for Directed Evolution of Fluorescent Proteins
2018 ·ACS Synthetic Biology ·Yutaka Saitō, (unknown), Hikaru Nakazawa, (unknown), Tomoshi Kameda, Koji Tsuda, Mitsuo Umetsu
111
19
Fine-grained optimization method for crystal structure prediction
2018 ·npj Computational Materials ·Kei Terayama, Tomoki Yamashita, Tamio Oguchi, Koji Tsuda
25
20
Machine learning accelerates MD-based binding pose prediction between ligands and proteins
2017 ·Bioinformatics ·Kei Terayama, Hiroaki Iwata, Mitsugu Araki, Yasushi Okuno, Koji Tsuda
19

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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