Tomohide Masuda
Impact in
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- Computational Drug Discovery Methods
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- Protein Structure and Dynamics
- Bioinformatics and Genomic Networks
- Chemical Synthesis and Analysis
- Genetics, Bioinformatics, and Biomedical Research
- vaccines and immunoinformatics approaches
Papers in
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- Machine Learning in Materials Science 3
- Lanthanide and Transition Metal Complexes 2
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- Protein Structure and Dynamics 3
- Co-authors
- David Ryan Koes (3 shared papers)Matthew Ragoza (2 shared papers)Jocelyn Sunseri (2 shared papers)Paul Francoeur (2 shared papers)Andrew T. McNutt (1 shared paper)Rishal Aggarwal (1 shared paper)Rocco Meli (1 shared paper)I. M. Snyder (1 shared paper)
- Journals
- Journal of Chemical Information and Modeling (3 papers)The Journal of Physical Chemistry A (2 papers)Journal of Cheminformatics (1 paper)Scientific Reports (1 paper)Chemical Science (1 paper)
- Partner nations
- JapanUnited StatesIndia
In The Last Decade
Tomohide Masuda
9 papers receiving 722 citations
Tomohide Masuda's Hit Papers
Peers
Comparison fields: 5 of 101
- Computational Theory and Mathematics 476
- Molecular Biology 495
- Materials Chemistry 246
- Pharmacology 67
- Organic Chemistry 79
Countries citing papers authored by Tomohide Masuda
This map shows the geographic impact of Tomohide Masuda'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 Tomohide Masuda with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tomohide Masuda more than expected).
Fields of papers citing papers by Tomohide Masuda
This network shows the impact of papers produced by Tomohide Masuda. 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 Tomohide Masuda. The network helps show where Tomohide Masuda may publish in the future.
Co-authors
The 25 scholars most cited alongside Tomohide Masuda, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | GNINA 1.0: molecular docking with deep learning Hit paper breakdown → | 2021 | 397 |
| 2 | 2020 | 175 | |
| 3 | 2022 | 111 | |
| 4 | 2020 | 31 | |
| 5 | 2014 | 16 | |
| 6 | 2016 | 11 | |
| 7 | 2025 | 1 | |
| 8 | 2024 | 1 | |
| 9 | 2020 | 1 | |
| 10 | 2025 | 0 |
About Tomohide Masuda
Tomohide Masuda is a scholar working on Materials Chemistry, Molecular Biology, Computational Theory and Mathematics, Organic Chemistry and Inorganic Chemistry, having authored 10 papers that have together received 744 indexed citations. Recurring topics across this work include Computational Drug Discovery Methods (5 papers), Machine Learning in Materials Science (3 papers), Protein Structure and Dynamics (3 papers), Lanthanide and Transition Metal Complexes (2 papers), Magnetism in coordination complexes (1 paper), Electrochemical sensors and biosensors (1 paper), Advanced Chemical Physics Studies (1 paper) and SARS-CoV-2 and COVID-19 Research (1 paper). The work is most often cited by research in Computational Theory and Mathematics (476 citations), Molecular Biology (495 citations), Materials Chemistry (246 citations), Pharmacology (67 citations) and Organic Chemistry (79 citations). Tomohide Masuda has collaborated with scholars based in Japan, United States and India. Frequent co-authors include David Ryan Koes, Matthew Ragoza, Jocelyn Sunseri, Paul Francoeur, Andrew T. McNutt, Rishal Aggarwal, Rocco Meli, I. M. Snyder, Satoshi Yabushita and Atsushi Nakajima. Their work appears in journals such as Journal of Chemical Information and Modeling, The Journal of Physical Chemistry A, Journal of Cheminformatics, Scientific Reports and Chemical Science.
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.