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Physical Organic Chemistry and Molecular Design Lab
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The lab designs molecules by combining chemistry and information science.
AboutAbout the lab
Research at the intersection of chemistry and information science
The Physical Organic Chemistry and Molecular Design Lab conducts data-driven research at the intersection of chemistry and information science.
Aiming to accelerate the discovery and design of new molecules and materials, the lab organizes information on chemical structures and works to make it available to people around the world.
Based on the ideas of physical organic chemistry, the lab works on the design of molecules and reactions by combining experiments, quantum chemical calculations, and machine learning.
The lab's website introduces it under the name "POCLab."
Key terms
- Physical organic chemistry
- A field that explores the properties and reactivity of molecules based on their chemical structures
- Antioxidant capacity
- The ability to suppress reactive oxygen species. It is measured by assays such as the ORAC and DPPH methods
- Ferroptosis
- Iron-dependent cell death. It is attracting attention as a new cell death mechanism
NewsLab news
View More ↗ResearchResearch themes
This lab aims to accelerate the discovery and design of new molecules and materials by combining chemistry and information science. It runs multiple research projects in parallel, using experiments, computational chemistry, and machine learning to predict the properties and reactions of molecules.
*Illustrative imageRelationship between structure and activity of antioxidants
The lab analyzes structural information on food components such as polyphenols and interprets in a unified way the antioxidant capacity data that vary from one assay method to another. Using computational chemistry and machine learning, it aims to build a system that predicts antioxidant capacity and the ability to control ferroptosis.
*Illustrative imageExploring functional materials
Using methods that combine computational chemistry and machine learning, the lab aims to design, predict, and optimize new functional materials.
*Illustrative imagePredicting chemical information with natural language processing
The lab aims to use natural language processing to predict chemical information and apply it to molecular design and activity prediction.
*Illustrative imagePredicting the selectivity of ketone reactions
By combining experiments, computational chemistry, and machine learning, the lab predicts the selectivity of ketone reactions and analyzes how that selectivity is determined.
MembersFaculty
The members of the Physical Organic Chemistry and Molecular Design Lab consist of faculty members and doctoral, master's, and undergraduate students.As of 2026
- Associate ProfessorHiroaki Gotoh
VoicesStudent voices
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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: “Chlorophacinone-3D-molecular-model-engraved-render-CAS” MolGod Scientific / CC BY-SA 4.0 (cropped)
- Relationship between structure and activity of antioxidants: “Alaska wild berries” U.S. Fish and Wildlife Service / Public domain (cropped)
- Exploring functional materials: “Chemistry Solutions , Chemistry Department, Aligarh Muslim University” Khanahmedsam / CC BY-SA 4.0 (cropped)
- Predicting chemical information with natural language processing: “Computers in Chemistry Edit-a-Thon at the American Chemical Society I” Fourandsixty / CC BY-SA 4.0 (cropped)
- Predicting the selectivity of ketone reactions: “Organic Chemistry Lab by marthou” Marthou / CC BY-SA 4.0 (cropped)
- Related lab (Biophysical Chemistry and Structural Life Chemistry Lab): “Rhodopsin” AtikaAtikawa / CC BY-SA 4.0 (cropped)
- Related lab (Biophysical Chemistry and Structural Life Chemistry Lab): “ANKRD35 protein structure” 1. Yang Zhang. I-TASSER: Fully automated protein structure prediction in CASP8. Proteins, 77 (Suppl 9): 100-113, 2009. 2. Ambrish Roy, Jianyi Yang, Yang Zhang. COFACTOR: an accurate comparative algorithm for structure-based protein function annotation. Nucleic Acids Research, 40: W471-W477, 2012. 3. Jianyi Yang, Yang Zhang. I-TASSER server: new development for protein structure and function predictions, Nucleic Acids Research, 43: W174-W181, 2015. / CC BY-SA 4.0 (cropped)
- Related lab (Nakamura Lab): “Reverse osmosis desalination plant” James Grellier / CC BY-SA 3.0 (cropped)
- Related lab (Organic Synthetic Chemistry Lab): “Standard Schlenk flask” OMCV / Public domain (cropped)
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