This project aims to leverage machine learning algorithms to analyze CYP gene variants and their impact on drug metabolism and interactions, paving the way for personalized medicine in Pakistan. Given the increasing demand for precision medicine and pharmacogenomics, this research has significant implications for pharmaceutical R&D, clinical decision-making, and drug safety regulations.

In this project, we investigated the binding modes of inhibitors docked into the binding site of PTP1B enzyme along with the detailed analysis of the most potent complex by taking into account various parameters such as the hydrogen bonding. As discovery of novel inhibitor templates remains a clear need, the best-docked enzyme–inhibitor complex was subjected to molecular dynamics to further our understanding of the system. Simulation was performed to check the dynamicity of ligand moiety in the presence of its designated target protein. Both receptor and ligand are dwelling in a dynamic environment due to efficient hydrogen bonding pattern with respect to time which is indicated only by molecular dynamics simulation.