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Chemical Biology and IT Drug Discovery Lab
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The lab studies in silico drug discovery, using computational methods to support the discovery of new drugs.
AboutAbout the lab
Aiming to make drug discovery more efficient through the power of computation
The Chemical Biology and IT Drug Discovery Lab studies in silico drug discovery, which advances drug discovery through computer-based calculation.
The lab uses both ligand-based methods, which rely on information about compounds, and structure-based methods, which rely on the 3D structure of the target protein.
Using docking simulation, molecular dynamics (MD) simulation, and AI and machine learning, the lab works on supporting in silico drug discovery research and on developing a Hit to Lead system.
The lab's website introduces it under the name "Hirokawa Lab."
Key terms
- In silico drug discovery
- A drug discovery approach that uses computer-based calculation to search for and design candidate compounds for drugs
- Docking simulation
- A computation that predicts, on a computer, how a compound binds to a target protein
- Hit to Lead
- The stage in which a Hit compound with activity of about a few μM is developed into a Lead compound with activity of about several to several tens of nM
NewsLab news
A paper on building the structure of an olfactory receptor, with a doctoral student as first author, was published in J Comput Aided Mol Des
A paper on the structural distribution of PROTAC complexes, with a doctoral student as first author, was published in J Chem Inf Model
A doctoral student received the Best Poster Award at the CBI Society 2024 Annual Meeting
A paper with a doctoral student as first author was published in Scientific Reports
A paper on the SINCHO method, with a doctoral student as first author, was published in J. Chem. Inf. Model.
ResearchResearch themes
In recent drug discovery research, in silico drug discovery that combines bioinformatics and cheminformatics is attracting expectations. The lab aims to create new target molecules and sites of action for drug discovery, and to make the drug development process more efficient.
*Illustrative imageDrug discovery support for difficult sites of action
The lab builds workflows that integrate the in silico component technologies needed to solve a variety of problems, and supports drug discovery for targets across a wide range of difficulty. Examples include developing a treatment for early-stage lung adenocarcinoma and elucidating, through in silico analysis, how drugs act on the EP4 receptor. Both are part of supporting research under programs such as AMED-BINDS.
*Illustrative imageAI-driven Hit to Lead optimization
Moving from a Hit compound to a Lead compound has traditionally been done by hand by medicinal chemistry researchers. The lab is building a system in which AI automatically generates Lead compounds while taking into account the structural fluctuations of the protein.
*Illustrative imageDesign methods for peptides and PROTACs
Focusing on peptide drugs and PROTACs, the lab develops in silico drug discovery methods that contribute to rational design. Peptide design is a joint research project with the Akiyama Lab at the Tokyo Institute of Technology, and PROTAC design is carried out as part of the Fugaku project.
MembersFaculty
The members of the Chemical Biology and IT Drug Discovery Lab are faculty, and doctoral, master's, and undergraduate students.As of 2026
- ProfessorTakatsugu Hirokawa
- Assistant ProfessorRyunosuke Yoshino
- Assistant ProfessorYasunobu Yamashita
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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: “CSIRO ScienceImage 2589 Shape of Protein Backbone” division, CSIRO / CC BY 3.0 (cropped)
- Drug discovery support for difficult sites of action: “ADAR Protein” Csully222 / CC BY-SA 4.0 (cropped)
- AI-driven Hit to Lead optimization: “Visual Molecular Dynamics visualization” Toughpkh / Public domain (cropped)
- Design methods for peptides and PROTACs: “BSC-MareNostrum4-B” Vcarceler / CC BY-SA 4.0 (cropped)
- Related lab (Neurology Lab): “Automated confocal image reader” Neil Emans IPK / CC BY-SA 3.0 (cropped)
- Related lab (Hematology Lab): “Eosinophil blood smear” Bobjgalindo / CC BY-SA 4.0 (cropped)
- Related lab (Medical Mathematical Informatics Lab): “Laptop and stethoscope ( )” jfcherry / CC BY-SA 2.0 (cropped)
- Related lab (Bioinformatics Lab): “Eppendorf epMotion 5075 liquid handling robot” Magnus Manske / CC BY-SA 3.0 (cropped)
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