LAB LOGA page on LabLog, a review site (not the lab's official page)
Yamanishi Lab
Machine-translated · View original
A lab that applies big data and AI to drug discovery and medicine.
AboutAbout the lab
Analyzing medical and pharmaceutical big data to take on drug discovery
The Yamanishi Lab conducts research in bioinformatics and chemoinformatics, analyzing big data from medicine, pharmaceutical science, and life science.
For societal application, the lab works on AI drug discovery, precision medicine, regenerative medicine, and preventive medicine, and develops machine learning algorithms for these purposes.
Its research themes also include drug repositioning, which finds new therapeutic effects of existing drugs, and elucidating the mechanisms of action of foods and Kampo (traditional Japanese herbal) medicines.
The lab belongs to the Department of Complex Systems Science in the Graduate School of Informatics.
Key terms
- Drug target molecule
- A biomolecule that, when controlled by a drug, can lead to treatment of a disease.
- Docking simulation
- A representative computational technique for evaluating the binding affinity between a drug candidate compound and a protein.
- Drug repositioning
- A drug discovery strategy that finds new therapeutic effects of existing drugs and redevelops them as treatments for other diseases.
NewsLab news
A doctoral student won the Nagoya University Academic Encouragement Award
A chemoinformatics book co-authored by Prof. Yamanishi was published
A doctoral student won the Student Encouragement Award from Bioinformatics Japan
A book on the latest research in AI drug discovery, co-edited by Prof. Yamanishi, was published
A paper on an AI that predicts drug target molecules for diseases was published in Nature Communications
ResearchResearch themes
In drug development, finding the molecules that a drug should target is a key challenge. This lab develops machine learning methods that treat the links among diseases, biomolecules, and chemical compounds as a network, and automatically predict therapeutic target molecules and drug candidate compounds.
*Illustrative imageAI that predicts drug targets for rare diseases
By combining genomic information with gene function data, the lab builds gene expression profiles that reflect disease pathology. An AI then searches for proteins that counteract the disease state, predicting drug target molecules for diseases including rare diseases.
*Illustrative imagePredicting drug efficacy and side effects from protein structures
The lab analyzes the three-dimensional structures of human proteins obtained with AI technologies such as AlphaFold, using docking simulation and machine learning. It predicts new drug effects as well as toxicity and side effects, and also identifies proteins involved in side effects.
*Illustrative imageMachine learning that predicts the functionality of foods
Using big data in life science and pharmaceutical science, the lab explores interactions between compounds contained in foods and human proteins. This method comprehensively predicts the functionality and mechanisms of action of foods, taking into account the regulation of protein groups involved in disease.
*Illustrative imageAI that generates structures of drug candidate compounds
The lab develops deep learning models that, given omics information such as desired biological activity or gene expression, automatically output the structures of compounds with those properties. The models design molecules autonomously, also taking biological activity and toxicity information into account.
MembersFaculty
The members of the Yamanishi Lab are faculty, researchers, visiting researchers, technical assistants, and doctoral, master's, and undergraduate students.As of 2026
- ProfessorYoshihiro Yamanishi
- Project Assistant ProfessorSatoko Namba
VoicesStudent voices
Ratings and reviews reflect the experiences of individual current or former students, not the lab's official views. Whether a lab has reviews, or how many, says nothing about the lab itself.
Students who take part in an interview may receive a thank-you gift worth ¥1,000, regardless of what they say.
Student ratings (0 students)
--
--
--
--
--
--
Student voices answer questions like these
- What's great about this lab?
- What did you learn after joining?
- What's the vibe, and what is the professor like?
- Who is this lab for?
- Where do graduates go?
Your experience can help younger students choose their lab. Your name won't be shown.
Write a lab reviewAnonymous · Your draft is saved if you stopRelatedLabs similar to Yamanishi Lab
Aoki LabSchool of Informatics, Department of Natural InformaticsAims to capture life phenomena at diverse scales as dynamic …
Tsukamoto LabSchool of Informatics, Department of Natural InformaticsAims to capture life phenomena at diverse scales as dynamic …
Oota LabSchool of Informatics, Department of Natural InformaticsEducation and research aimed at elucidating life phenomena a…
Simulation Science LabSchool of Informatics, Department of Natural InformaticsAims to understand, through mathematical models, simulation,…The information on this page is compiled by the LabLog team from the lab's public information and interviews. For corrections or removal requests, please contact LabLog, not the lab.
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: “Benomyl-3D-molecular-model-engraved-render-CAS” MolGod Scientific / CC BY-SA 4.0 (cropped)
- AI that predicts drug targets for rare diseases: “DNA microarray chip” ErikAbner / CC BY 4.0 (cropped)
- Predicting drug efficacy and side effects from protein structures: “Protein Crystal Structure of the human Sec23a-24a heterodimer, complexed with the SNARE protein Sec22b PDB 2nup” Emw / CC BY-SA 3.0 (cropped)
- Machine learning that predicts the functionality of foods: “Food cooking ingredients - vegetables. (paco market, Manila)” Patrickroque01 / CC BY-SA 4.0 (cropped)
- AI that generates structures of drug candidate compounds: “Molecular Model Set for General Chemistry - DPLA - c8620ef6698fc55320c53575471c9177” Allyn and Bacon / Public domain (cropped)
- Related lab (
青木研究室 （Aoki Lab） ): “Physcomitrella” Pirex at English Wikipedia / Public domain (cropped) - Related lab (塚本研究室): “Round bottom flask” GOKLuLe / CC BY-SA 3.0 (cropped)
- Related lab (太田研究室
（OTA Laboratory） ): “Protein ARF4 (2) PDB 1z6x” Emw / CC BY-SA 3.0 (cropped) - Related lab (シミュレーション科学研究室
（北研究室） ): “This visualization shows what Einstein envisioned” Henze, NASA / Public domain (cropped)
About LabLog
LabLog is a lab review app for students only. Student members at each university interview students in labs and publish what they say. Student voices can only be read by students who sign up with a university email address. Operated by bestiee Inc.
Learn more about LabLog ↗Search engines and AI may reference the basic lab info and rating summaries on this page and the description of LabLog. Student voices are for student members only. Automated collection by programs (scraping, or input to or training of AI) is prohibited under Article 8 of the Terms of Service. If you need data for research or analysis, please contact us.