Review on Artificial Intelligence and Machine Learning in Drug Discovery and Development

Artificial Intelligence (AI) and Machine Learning (ML) are revolutionizing the field of drug discovery and development by providing innovative solutions to the challenges associated with traditional pharmaceutical research. Conventional drug development is a time-consuming, expensive and high-risk process that often requires more than a decade and substantial financial investment to bring a new drug to market. AI and ML technologies have the potential to streamline this process by enabling the rapid analysis of vast amounts of biological, chemical and clinical data. These advanced computational tools are widely applied in target identification, lead optimization, virtual screening, de novo drug design, drug repurposing and prediction of pharmacokinetic and toxicological properties. Machine learning algorithms can identify hidden patterns in complex datasets, improving the accuracy of predictions and reducing the need for extensive laboratory experimentation. AI-powered platforms also support clinical trial optimization through patient selection, disease prediction, and outcome forecasting, thereby increasing the probability of successful drug development. The integration of AI with big data analytics, genomics, proteomics and high-throughput screening technologies has accelerated the discovery of novel therapeutic candidates for various diseases, including cancer, infectious diseases, neurological disorders and rare genetic conditions. However, challenges such as data quality, model interpretability, regulatory compliance and ethical concerns regarding data privacy remain significant barriers to widespread adoption. This review discusses the principles, applications, benefits and limitations of AI and ML in drug discovery and development. The continued advancement of these technologies is expected to enhance research efficiency, reduce development costs and facilitate the delivery of safer, more effective and personalized medicines, ultimately transforming the future of healthcare.