Chemoinformatics

Graduate / advanced undergrad

Graduate / advanced undergrad

Semester: Spring ｜ Audience: graduate students in chemistry, pharmacy, and related interdisciplinary fields

Course Introduction

Chemoinformatics introduces methods for acquiring, managing, and computationally modeling chemical information—covering molecular representation and descriptors, chemical databases, quantitative structure–activity relationships (QSAR), and the chemoinformatics toolchain. It helps researchers efficiently organize molecular data with computational means and accelerate the translation from data to insight, serving as an important bridge between chemical experiments and data science.

Key Topics

• Molecular representation—from SMILES / molecular fingerprints to graph representations.
• QSAR—building and reliability-assessing quantitative structure–activity models.
• Chemical databases—management and retrieval of common public and local data.
• Chemical big data—pipelines for high-throughput and virtual screening.

Teaching Format

Lectures + hands-on practice + TBD (course projects / literature reports, etc.).

For reference materials or lecture invitations, feel free to write.