Bioinformatics
We investigate RNA structure and function from sequence data and explore the mechanisms of life through genome analysis.
RNA structure prediction · Genome analysis · Machine learning and optimization
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We seek to uncover the mechanisms of life hidden in biological data. At the Sato Laboratory, AI, machine learning, and algorithm development connect our research on RNA and genome analysis with drug discovery and the use of medical data.
Research areas
In Silico Drug Discovery
We connect molecular structures with their properties through machine learning to find and design potential drug candidates.
Molecular representation learning · Virtual screening · RNA-targeted drug discovery
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Medical AI
We aim to use medical and genomic data to support personalized healthcare and develop AI methods that protect privacy.
Medical–engineering collaboration · Precision medicine · Privacy protection
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Our approach
- Start with a biological question
- What structure does an RNA form? How can we predict a compound's properties? We turn questions in life science into problems that can be addressed computationally.
- Combine knowledge with data
- We combine machine learning with thermodynamics, molecular structure, and mathematical optimization to develop algorithms suited to biological data.
- Validate and share our methods
- We evaluate predictions on previously unseen data and share our work through publications, open-source software, and web servers.
Open research tools
Our RNA structure prediction methods are available as web servers that you can use directly in your browser.
MXfold2
RNA secondary structure prediction integrating deep learning and thermodynamics.
IPknot
Prediction of complex RNA secondary structures, including pseudoknots.