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Takashi Ishida Lab
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The lab uses machine learning to analyze large-scale life science data, with the aim of advancing drug discovery and medicine.
AboutAbout the lab
Solving the mysteries of life through computation.
The Takashi Ishida Lab develops computational methods for solving problems in biology.
Its main focus areas are protein structure prediction, drug discovery, and applications of machine learning to biology.
The lab builds methods based on machine learning, probabilistic models, and algorithm design, aiming for algorithms that are fast, accurate, and interpretable.
In recent years, through collaborations with the School of Medicine and the School of Dentistry, the lab has also been analyzing clinical data.
Key terms
- Bioinformatics
- A field that uses computers and information science methods to interpret large amounts of data from biology and medicine
- Protein 3D structure
- The three-dimensional shape of a protein, formed when a chain of amino acids folds
- Retrosynthesis
- Working backward from the molecule you want to make to determine which starting materials to combine, and how, to produce it
NewsLab news
Paper on an AI that predicts the risk of eye disease published in Ophthalmol. Retina
Paper on survival analysis combined with machine learning published in IEEE Access
Paper compiling chemical synthesis data published in J. Cheminform.
ResearchResearch themes
Working at the boundary between computer science and biology, the lab develops computational methods based on machine learning, probabilistic models, and algorithm design to address fundamental questions in biology. In recent years, through collaborations with the School of Medicine and the School of Dentistry, it has also been analyzing clinical data.
*Illustrative imageProtein Structure Prediction
This research predicts the three-dimensional structure of a protein from its amino acid sequence. The lab has worked on template-based modeling, which uses similar known structures as templates, including a method that uses machine learning to find the alignment with a template and a method that selects the best models from the candidates generated.
*Illustrative imageRetrosynthesis Prediction
This research starts from a target molecule and traces possible synthesis routes backward. The lab identifies where to break the molecule based on its substructures, and ranks candidate starting materials in order of structural similarity. It is also building a public database that consolidates synthesis data in one place.
*Illustrative imageBad Breath Prediction
In collaboration with the School of Dentistry, the lab is developing a deep learning method that predicts bad breath from photographs of the tongue. Using explainable AI techniques, it shows which features drive the prediction, such as the color, thickness, and extent of the tongue coating.
MembersFaculty
The members of the Takashi Ishida Lab are faculty and students in the doctoral, master's, and undergraduate programs.As of 2026
- ProfessorTakashi Takashi IshidaPublications ↗
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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: “4xhe pinnatoxin pentamer” Opabinia regalis / CC BY-SA 4.0 (cropped)
- Protein Structure Prediction: “Protein CAPN9 PDB 1ziv” Emw / CC BY-SA 3.0 (cropped)
- Retrosynthesis Prediction: “Sodium tartrate ball-and-stick” MarinaVladivostok / CC0 (cropped)
- Bad Breath Prediction: “A dental chair in a dentist clinic in North Carolina, United States” Harrison Keely / CC BY 4.0 (cropped)
- Related lab (大上研究室): “Protein Crystal Structure of the human Sec23a-24a heterodimer, complexed with the SNARE protein Sec22b PDB 2nup” Emw / CC BY-SA 3.0 (cropped)
- Related lab (関嶋研究室): “Bedaquiline 3D” Jovan.gec / CC BY-SA 4.0 (cropped)
- Related lab (秋山研究室): “Protein filament” Opabinia Regalis / CC BY-SA 3.0 (cropped)
- Related lab (村田研究室): “Spider web Luc Viatour” Luc Viatour / CC BY-SA 3.0 (cropped)
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