Sunisha Kulkarni

Drug discovery is one of the most resource-intensive endeavours in modern science, requiring over 12 years and USD 2.6 billion on average to bring a single drug to market, with a clinical failure rate exceeding 90%. Artificial Intelligence (AI) is fundamentally transforming this process. This review examines how AI technologies — including machine learning, deep learning, graph neural networks, generative models, and natural language processing — are being applied across every stage of the drug discovery pipeline, with particular focus on drug repurposing. Key real-world case studies are analysed, including DeepMind’s AlphaFold2, which predicted over 200 million protein structures; Insilico Medicine’s AI-designed pulmonary fibrosis candidate developed in approximately 30 months; and BenevolentAI’s identification of baricitinib as an FDA-approved COVID-19 treatment. Advantages including accelerated timelines and improved molecular design are considered alongside persistent limitations in data quality, model interpretability, and regulatory frameworks. The review concludes with implications for pharmacy education and future directions including foundation models, multimodal AI, and quantum-enhanced simulation.

The abstract presents an overview of advancements in drug delivery systems, focusing on the evolution from conventional methods (like tablets, capsules, and syrups) to more sophisticated controlled delivery approaches. It emphasizes the limitations of traditional drug delivery, including poor bioavailability, inconsistent drug levels in the body, and the inability to sustain therapeutic effects. These shortcomings can make treatments less effective and potentially unsafe. To address these issues, controlled drug delivery systems (CDDS) have been developed, which allow for precise and sustained release of medication at targeted sites. Over the past two decades, these systems have evolved significantly; incorporating innovations at both the macro and nano scales, and now include intelligent systems that can respond to stimuli for targeted drug delivery. Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas. It also covers the fundamental aspects of drug delivery, exploring the pharmacokinetics involved, limitations of conventional methods, and the design and classification of CDDS. It also delves into cutting-edge topics such as nano-drug delivery, targeted therapy, and the use of smart biomaterials, concluding with a discussion of current challenges and future research directions in the field.