About me
I am currently working as a postdoctoral research scientist at University of Maryland Baltimore in the field of Theoretical and Computational Chemistry. After finishing higher secondary education from my hometown Dehri-on-sone, I joined Banaras Hindu University (2006-09) for Bachelor's degree with honors in Chemistry. In my third year, I developed keen interest in Quantum Chemistry which I envisioned to pursue further. For my masters degree, I joined University of Hyderabad (2009-11), where I learnt a great deal of theoretical chemistry and its applications. I joined Prof. Shridhar Gadre for my Ph.D. degree at IIT Kanpur (2011-17), where I developed models for molecular electrostatic potential which includes lone pairs, electronegativity and zero flux surface in electric field of molecules. We implemented these topological analysis features in software package DAMQT which we continue to develop. Carrying forward my skills in methodology development as a postdoc, I am contributing to in the area of molecular dynamics, CHARMM Additive and Drude Force-Field development and QMMM techniques. Here we have developed two softwares, FFParam for force-field parametrization, CHARMM-Psi4 Interface for QMMM molecular dynamics and a neural-network based package, DGenFF for predicting atomic partial charges and atomic polarizabilities in Drude Polarizable Force-Field format with remarkable speed and QM-level accuracy. I am also handling computer aided drug-discovery project for treatment of cancer.
For more than 10 years, I have been involved in methodology and software development related to a range of quantum mechanical and molecular mechanical concepts. In future, I envision to apply quantum chemical, molecular mechanical and machine learning techniques to improve the accuracy of computational drug-design processes.
For more than 10 years, I have been involved in methodology and software development related to a range of quantum mechanical and molecular mechanical concepts. In future, I envision to apply quantum chemical, molecular mechanical and machine learning techniques to improve the accuracy of computational drug-design processes.