Bian Li
About Bian Li
Bian Li is a Postdoctoral Fellow at Vanderbilt University, specializing in computational chemistry, genomics, and machine learning to study protein-X interactions and genetic variations.
Current Position at Vanderbilt University
Bian Li is currently a Postdoctoral Fellow at Vanderbilt University in the Greater Nashville Area, TN. In this role, Bian Li engages in cutting-edge research combining computational chemistry, genomics, and machine learning to study protein interactions and human genetic variations.
Previous Role at Yale University
Bian Li previously worked at Yale University as a Postdoctoral Associate from 2018 to 2019. During an 11-month tenure in New Haven, Connecticut, Bian Li contributed to various research projects, applying advanced computational techniques to investigate molecular mechanisms and protein structures.
Educational Background in Computational Chemistry and Medicinal Chemistry
Bian Li earned a Doctor of Philosophy (PhD) degree in Computational Chemistry from Vanderbilt University, studying from 2013 to 2018. Prior to this, Bian Li completed a Master of Science (MS) degree in Medicinal Chemistry at Fudan University from 2010 to 2013, and a Bachelor of Science (BS) degree in Pharmaceutical Science from Shanghai Jiao Tong University from 2006 to 2010.
American Heart Association Support
Bian Li's current research endeavors at Vanderbilt University are supported by the American Heart Association. This support enables continued exploration into the molecular underpinnings of disease-causing genetic variants and individual differences in drug responses using advanced computational techniques.
Research Focus: Computational Chemistry and Genomics
Bian Li utilizes an interdisciplinary approach that includes computational chemistry, genomics, and machine learning in research. The primary focus is on understanding human genetic variation in the context of protein structures and elucidating the molecular mechanisms behind disease-causing genetic variants. Additionally, Bian Li investigates inter-individual differences in drug response, aiming to improve personalized medicine.