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.
Congratulations to Ilona on winning the prestigious Croucher Fellowships for Postdoctoral Research! Thanks for the support from the Croucher Foundation! Ilona’s Croucher profile page is available here: https://scholars.croucher.org.hk/scholars/unarta-ilona-christy
Our paper: “Markov state models to study the functional dynamics of proteins in the wake of machine learning” published in JACS Au has been highlighted in its inaugural virtual issue: “Markov state models to study …
Our collaborative work with Vivian Yam’s group on elucidating the role of Pt···Pt interactions in the self-assembly of platinum(II) complexes has been published in PNAS. Congratulations to Xiaoyan, Siqin, Fu Kit, Chu, and Eshani!
Our paper on the encoder-neural-network based “RPnet” method for coarse-graining protein dynamics via the reverse projection of transition modes has been selected as a HOT article by Phys. Chem. Chem. Phys.
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