Welcome to the Huang Research Group!
The main goal of our lab is to understand and manipulate biomolecular dynamics by developing and applying novel statistical mechanics based methods that can bridge the gap between experiments and simulations.
Examples of our interested research areas include elucidation of functional conformational changes in gene transcription, elucidation of molecular recognition and self-assembly, development of Markov State Model and Generalized Master Equation model for biomolecular dynamics, development of Integral Equation theories for solvation, and development of deep learning methods to predict protein-ligand and protein-RNA interactions.
Group News!
Jan 2025
Congratulations to Bojun, Jordan, and Ilona on publishing the TS-DAR method in Nature Communications! Many thanks as well to our collaborators, Prof. Sharon Li and Xuefeng. The TS-DAR method simultaneously identifies transition states across multiple …
Nov 2024
We’re excited to welcome 3 new graduate students: Chengwei Dong (Chemistry, B.S. from Peking U.), Peter Swanson (Chemistry, B.S. from UNE, jointly supervised with Arun Yethiraj), and Andres Lira (Biophysics, B.S. from UC Davis, jointly …
June 2024
Our lab (PI) receive a Research Forward award for funding our development of a chemical feature-based Transformer platform to predict molecular glues! Excited to collaborate with Prof. Sharon Li (UW-Madison, CS) and Weiping Tang (UW-Madison, Pharmacy)!
March 2024
Our tutorial on building non-Markovian models (qMSMs, IGME) from MD simulations for protein dynamics was published as a featured and cover article at JCP. Please try it out on GitHub: https://github.com/xuhuihuang/GME_tutorials!
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