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Our research is in the area of Theoretical Chemistry and Molecular Biophysics. The operation of biological molecules is a highly dynamic process that relies on numerous functional conformational changes. We are interested in elucidating the dynamics of these conformational changes by developing new methods based on statistical mechanics that can bridge the gap between experiments and atomistic molecular dynamics (MD) simulations.
We believe that tight integration of analytical theories with modern computer simulations and machine learning techniques can lead to breakthroughs in many aspects of theoretical chemistry. We also believe that establishing links between molecular mechanisms of bio-systems and their cellular and genome-wide behaviors is key to understanding many fundamental biological processes. Our group is currently working on the following directions:
1. Methodological advances of the Markov State Models (MSMs) and Generalized Master Equation for bimolecular dynamics.
2. Elucidation of functional conformational changes of RNA Polymerases.
3. Elucidation of dynamics for molecular recognition and self-assembly.
4. Development of new Integral Equation Theories for solvation.
5. Development of deep-learning based algorithms for to predict novel inhibitors against desired protein targets.
6. Elucidation of structural ensemble from heterogeneity Cryo-EM datasets.