My academic background combines expertise in medicinal chemistry, computational drug discovery, and artificial intelligence-driven approaches for pharmaceutical research. My research focuses on the application of chemoinformatics, machine learning, molecular modeling, and data analysis to support rational drug design and accelerate the discovery of novel therapeutic agents. Through my doctoral work, I explore predictive modeling strategies, including QSAR, molecular docking, and AI-based methodologies, to identify and optimize bioactive compounds with improved efficacy and safety profiles. I am particularly interested in integrating computational and medicinal chemistry approaches to address challenges in drug discovery and development. My long-term goal is to contribute to the design of innovative, safer, and more effective medicines by leveraging advanced data-driven technologies. I welcome opportunities for scientific collaboration and interdisciplinary research in both academic and industrial settings. Researchers and professionals interested in chemoinformatics, drug discovery, artificial intelligence, or medicinal chemistry are encouraged to connect and exchange ideas.