Artificial Intelligence Drug Discovery and Development - Subedar Ikra A; Bhandare Sangita N. (Asian Journal of Pharmaceutical Research and Development, 2025)
The integration of Artificial Intelligence (AI) into drug discovery and development has revolutionized the pharmaceutical landscape by enabling faster, cost-effective, and data-driven innovations. Traditional drug discovery methods are time-consuming and expensive, often requiring over a decade and billions of dollars to bring a new drug to market. AI technologies such as Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) significantly enhance each stage of the drug discovery pipeline—from target identification and virtual screening to lead optimization, toxicity prediction, and clinical trial design. By analyzing complex biological, chemical, and clinical datasets, AI facilitates the discovery of novel therapeutic targets, predicts molecular interactions, and improves drug safety and efficacy. Moreover, generative AI models accelerate de novo drug design, while AI-driven analytics enable personalized medicine and drug repurposing. Despite challenges such as data bias, interpretability, and regulatory constraints, AI’s transformative potential continues to reshape pharmaceutical research, offering a faster, more efficient, and precise approach to developing next-generation therapeutics.