eSupervisor Detail

About me

Computational chemist with expertise in molecular modeling, molecular dynamics, QM/MM simulations, enhanced sampling, and AI-driven drug discovery. Experienced in studying protein–ligand interactions, enzyme catalysis, and biomolecular dynamics using GROMACS, PLUMED, Maestro, and Python-based workflows. Research interests include computational drug discovery, enzyme engineering, free energy calculations, and machine learning-integrated biomolecular simulations.

Interests: My research interests focus on computational chemistry, molecular modeling, biomolecular simulations, and AI-driven drug discovery. I am particularly interested in understanding the molecular mechanisms governing protein–ligand interactions, enzyme catalysis, conformational dynamics, and biomolecular recognition using atomistic simulations and enhanced sampling techniques. My work integrates classical molecular dynamics, QM/MM methods, free energy calculations, metadynamics, and machine learning approaches to investigate complex chemical and biological systems with high precision. My areas of focus include computational drug discovery, enzyme engineering, biomolecular interaction networks, protein conformational dynamics, free energy landscape exploration, and simulation-driven mechanistic studies. I am also interested in integrating machine learning and data-driven approaches with molecular simulations to accelerate the identification of functional molecular descriptors, reaction coordinates, and predictive models for biomolecular behavior. In addition, I am keen on developing computational workflows that bridge fundamental molecular understanding with translational applications in therapeutics, biomaterials, and sustainable biochemical technologies.