Intelligent Drug Design: The Expanding Role of Artificial Intelligence in Modern Therapeutics

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in modern drug discovery and rational drug design, addressing the limitations of conventional pharmaceutical development, which is often time-intensive, costly, and associated with high attrition rates. By leveraging machine learning, deep learning, and advanced data mining techniques, AI enables the analysis of large-scale biological, chemical, and clinical datasets to accelerate target identification and validation. AI-driven platforms facilitate prediction of drug-target interactions, high-throughput virtual screening, and prioritization of promising lead compounds with enhanced precision. Additionally, predictive models for pharmacokinetic and pharmacodynamic parameters, including absorption, distribution, metabolism, excretion, and toxicity (ADMET), help minimize late-stage clinical failures and optimize candidate selection. In drug design, AI strengthens both structure-based and ligand-based approaches by refining molecular properties, improving binding affinity, and enhancing safety profiles. Applications such as molecular docking, de novo molecular design and generative modelling enable the rapid development of novel therapeutic entities. Furthermore, AI significantly contributes to drug repurposing by identifying new therapeutic indications for existing drugs, thereby reducing development timelines and costs. Overall, AI-driven methodologies are reshaping pharmaceutical research by enhancing efficiency, precision, and innovation. With continuous advancements in computational power and data integration, AI holds immense potential to revolutionize next-generation therapeutics and precision medicine.