ARTIFICIAL INTELLIGENCE IN DRUG DISCOVERY: TRANSFORMING PHARMACOLOGY THROUGH DATA-DRIVEN INNOVATION

*Ashel Rovita L., Avrin Romitha L. and Midhuna K.

ABSTRACT

Drug development has long been a slow, expensive, and high-risk journey, but Artificial Intelligence (AI) is rapidly changing that narrative. This review explores how AI is reshaping pharmacology by making the process faster, smarter, and more personalized. From identifying potential drug candidates through advanced virtual screening tools like AtomNet and DeepDock, to improving molecular activity predictions with machine learning-driven QSAR models, AI is enhancing key stages of discovery. It's also proving valuable in drug repurposing—uncovering new uses for existing drugs using data mining, natural language processing, and network analysis, as seen in breakthroughs like baricitinib for COVID-19 and metformin for Alzheimer’s. Beyond development, AI is improving drug safety through real-time pharmacovigilance, detecting adverse effects from sources like electronic health records and social media. Still, challenges such as opaque “black box” models, biased data, and regulatory uncertainty remain. Even so, innovations like explainable AI and digital twin technology are paving the way for a more transparent, efficient, and individualized approach to medicine.

Keywords: Artificial Intelligence (AI), Drug Discovery, Drug Repurposing, Virtual Screening, QSAR Modeling.