I am  M.Sc. Biotechnology student with a research focus on computer-aided drug discovery, currently exploring molecular dynamics simulations to understand biomolecular interactions. My ongoing project, “Identifying Para-aminosalicylic Acid Resistance Mechanism in Mycobacterium tuberculosis,” uses MD simulations to investigate structural changes linked to drug resistance.
I have hands-on experience in molecular docking (AutoDock, PyRx, Discovery Studio) and beginner-level skills in Python for automating basic computational tasks. My goal is to integrate docking and dynamics for deeper insight into drug-target behavior and resistance mechanisms.

I am currently pursuing M.Sc. Biotechnology (DBT supported) in Institute of Advanced Research, Gandhinagar with a research specialization in computer-aided drug discovery. My work focuses on Quantitative Structure-Activity Relationship (QSAR) modeling integrated with machine learning. My ongoing research project is on “2D-QSAR-Based Screening of Potential Inhibitors for the MDM2 Oncoprotein”. I have practical experience in molecular docking, and I’m building proficiency in Python scripting to automate computational workflows. My long-term objective is to develop a user-friendly and high-performance QSAR modeling platform that enhances the speed and accuracy of drug discovery pipelines.

I am currently pursuing M.Sc. in Biotechnology with a research focus on computer-aided drug discovery, particularly in Quantitative Structure-Activity Relationship (QSAR) modeling using machine learning methods. My ongoing project is titled as “Identifying Inhibitory Molecules of the DAPK1 Protein through QSAR Approaches.” I have practical experience in molecular docking using tools like AutoDock and PyRx, and I possess beginner-level Python skills for automating basic computational workflows. My long-term aim is to develop an advanced, user-friendly interface for accurate and efficient QSAR modeling.