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!
June 2025
Congratulations to Bojun, Siqin, Jordan, and Mingyi on publishing MEMnets in Nature Computational Science! MEMnets is a deep learning framework for coarse-graining protein dynamics, driven by a new statistical mechanics theory to minimize memory kernels. …
May 2025
Congratulations to Jordan Boysen on winning the 2025 ACS Division of Physical Chemistry Undergraduate Award!
May 2025
Congratulations to Bojun Liu from our group on winning the APL Computational Physics Best Poster Award at the 55th Midwest Theoretical Chemistry Conference (MWTCC55)!
April 2025
Our group has been awarded a new NSF grant from Division of Chemistry to develop a polarizable 3DRISM implicit solvent model for AMOEBA solutes to enable efficient and accurate RNA simulations!
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