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

  • Development of Implicit Solvent Models

  • Calculation of Free Energies

Bioinformatics

  • Development of Computational Pipelines for Analyzing and Mining Omics Data

  • High Performance Analysis of Omics Data

RESEARCH INTERESTS

Computational Biophysics

  • Development of Solvent Models

  • Calculation of Free Energies

Bioinformatics

  • Development of Computational Pipelines for Analyzing and Mining Omics Data

  • High Performance Analysis of Omics Data

Computational Geometry

  • Triangulation

  • 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

  • Parallel Computation

  • Cloud Computing

Computational Biology

  • Structural Computational Biology

  • Protein-Ligand Interaction

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