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Uncovering pharmacological principles of TCM with Machine Learning


In recent years, deciphering TCM from the big data perspective becomes a hot research topic. Many research focuses on network pharmacology and data mining of TCM. However, even the largest TCM database, Traditional Chinese Medicine Integrated Database (TCMID), has not included enough information about herbs, and the structure of TCM theory also has not been explored by AI algorithms. 

In this project, we plan to collect and clean TCM prescriptions for form a large TCM herbal network dataset, and carry out in-depth analysis using machine learning methods. We will look for low-dimensional representations of TCM prescriptions and seek their interpretation in terms of TCM attributes. The analysis should yield a set of quantitative rules with regard to the compatibility of herbal ingredients in a prescription, and “modules” of herbal combinations that deliver specific therapeutic effects. Together they will provide quantitative underpinning of TCM pharmacological principles, paving the way for AI-based TCM prescription.

Required skills

The ideal candidate is expected to have a background in one or more of the following fields: physics, biology, computer science, or mathematics. Excellent organizational and interpersonal skills are essential. Computer programming and analytic experience with large datasets is a plus.

Principal Investigator


Dr. Tian

Dr. Liang Tian

Assistant Professor, Department of Physics




Prof. Aiping Lyu

Dean of School of Chinese Medicine
Dr. Kennedy Y.H. Wong Endowed Chair of Chinese Medicine

Prof. Tang

Prof. Leihan Tang

Professor, Department of Physics