Artificial Intelligence in Drug Discovery and Development: Current Status, Challenges and Future Prospects

Abstract

The review article examines the transformative influence of Artificial Intelligence (AI) in redefining the paradigms of drug discovery and pharmaceutical development. It explores how progressive innovations in AI methodologies including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Reinforcement Learning (RL), Graph Neural Networks (GNN), and Generative AI are being strategically harnessed to address critical bottlenecks in the drug development continuum. Key areas of focus include target identification, lead molecule exploration, rational drug design, optimization strategies, and clinical trial orchestration. The review highlights AI's potential to revolutionize the pharmaceutical landscape by expediting drug discovery trajectories and curtailing the costs associated with introducing novel therapeutic entities.