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Sato Lab
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The lab uses information science and data science to unravel the mysteries of life.

AboutAbout the lab
Unraveling the mysteries of life with information science.
The Sato Lab studies data-driven bioinformatics using artificial intelligence and machine learning.
The lab develops advanced algorithms for analyzing life science and medical data, with the aim of accelerating research in the life sciences.
For RNA secondary structure prediction, it has developed methods such as MXfold2, which uses deep learning.
The lab also works on drug discovery informatics, which uses computers to find new drug candidates, and on medical AI.
Key terms
- Bioinformatics
- A research field that uses the methods of information science to analyze the large amounts of data produced in the life sciences.
- RNA secondary structure
- The folded shape formed when an RNA strand partially pairs with itself. It is closely related to the RNA's function.
- Deep learning
- A machine learning method that uses neural networks with many stacked layers to learn features from large amounts of data.
NewsLab news
Research paper on RNA 3D structure prediction published in Proteins
Review article on RNA interaction prediction published in a Springer book
Research paper on RNA secondary structure prediction using deep learning published
The Sato Lab moves to the School of Life Science and Technology, Tokyo Institute of Technology
ResearchResearch themes
With the aim of unraveling the mysteries of life through information science and data science, the lab develops advanced algorithms for analyzing life science and medical data. Its research rests on three pillars: bioinformatics, drug discovery informatics, and medical AI.
*Illustrative imageBioinformatics
The lab works on machine learning for the life sciences. It has developed MXfold2, an RNA secondary structure prediction method that combines deep learning with a thermodynamic model, and IPknot, which can rapidly predict even pseudoknots, a complex type of substructure.
*Illustrative imageDrug discovery informatics
This research uses computers to find new drug candidates. Beyond using machine learning to predict compounds that bind to proteins, the lab aims to develop methods that use deep generative models to automatically design previously unknown compounds likely to fit.
*Illustrative imageMedical AI
The lab aims to put machine learning methods that make use of big medical data into practical use in society. It works on precision medicine, which predicts drug effects from genetic test results, and on learning methods that protect personal information using techniques such as differential privacy.
MembersFaculty
The members of the Sato Lab include faculty, researchers, doctoral and master's graduate students, research students, undergraduates, and technical support staff.As of 2026
- ProfessorKengo SatoPublications ↗
- Assistant ProfessorSho TsukiyamaPublications ↗
- Project Professor浅井 潔
- Project Associate ProfessorNaoki OsatoPublications ↗
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Report an errorAbout the images
The top and research theme images are illustrative and were not taken at the lab. They are cropped from Wikimedia Commons images. Cropped CC BY-SA images are provided under the same CC BY-SA license as the original (version shown on each line).
- Top image: “Aminoacyl-tRNA synthetase in complex with cognate tRNA” Oleksandr V. Savytskyi, Yaroslav V. Katrich / CC BY 4.0 (cropped)
- Bioinformatics: “1G62 S cerevisiae” Ajpolino / CC BY 4.0 (cropped)
- Drug discovery informatics: “Pills and medicines 01” BuhaM / CC BY-SA 4.0 (cropped)
- Medical AI: “Artificial Neural Network with Chip” mikemacmarketing / original posted on flickr Liam Huang / clipped and posted on flickr / CC BY 2.0 (cropped)
- Related lab (伊藤研究室): “CSIRO ScienceImage 11313 The CSIRO GPU cluster at the data centre” division, CSIRO / CC BY 3.0 (cropped)
- Related lab (山田研究室): “E coli at 10000x, original” Photo by Eric Erbe, digital colorization by Christopher Pooley, both of USDA, ARS, EMU. / Public domain (cropped)
- Related lab (藤島研究室): “Miller-Urey experiment JP” KuriPop / CC BY-SA 3.0 (cropped)
- Related lab (梶川研究室): “Reverse transcriptase 1KLF” Thomas Splettstoesser (www.scistyle.com) / CC BY-SA 3.0 (cropped)
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