RESEARCH
In pharmaceutical applications and structure-based drug discovery, it is necessary to have accurate yet fast prediction of the binding free energy of drugs to biomolecular targets. My research goal is to facilitate such processes by developing efficient computational methods. Most promising models will be implemented as open source code, in AMBER molecular dynamics tools (freely available at http://ambermd.org/).
RESEARCH INTERESTS
Computational Biophysics
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Development of Implicit Solvent Models
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Calculation of Free Energies
Bioinformatics
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Development of Computational Pipelines for Analyzing and Mining Omics Data
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High Performance Analysis of Omics Data
RESEARCH INTERESTS
Computational Biophysics
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Development of Solvent Models
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Calculation of Free Energies
Bioinformatics
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Development of Computational Pipelines for Analyzing and Mining Omics Data
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High Performance Analysis of Omics Data
Computational Geometry
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Triangulation
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Shape Reconstruction
SELECTED PROJECTS
Global Optimization of Biomolecular Surface
Apr. 2017- Present
Optimization of atomic radii in an implicit solvent model to obtain close agree- ment with experimental results in terms of calculating electrostatic binding free energies. The underlying massively parallel optimization method, VTDIRECT, runs on Virginia Tech supercomputers.
Application of Deep Learning in Calculation of Binding Free Energies
Oct. 2019- Present
Development of a physics-based deep learning network to calculate binding free energies of small protein-ligand complexes in an implicit solvent model. We build our model based on a machine learning framework called Deepchem, a python library mainly used in drug discovery processes.
High Performance Computing
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Parallel Computation
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Cloud Computing
Computational Biology
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Structural Computational Biology
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Protein-Ligand Interaction