Artificial intelligence in drug discovery and development: A comprehensive review

Abstract

Artificial intelligence (AI) has significantly transformed drug discovery and development by accelerating processes, reducing costs, and enhancing precision. AI-driven methods, such as machine learning (ML), deep learning (DL), and natural language processing, are employed at various stages of the drug pipeline, including target identification, lead compound selection, and clinical trial optimization. AI’s ability to analyze large datasets enables researchers to uncover disease mechanisms, design novel compounds, and predict key properties, such as toxicity and bioactivity. Generative AI models further enhance drug discovery by creating new molecules from scratch. In addition, AI contributes to optimizing clinical trials, predicting adverse events, and supporting drug repurposing efforts. While AI offers numerous advantages, challenges, such as data quality, regulatory concerns, and the integration of AI with existing systems must be addressed. The future of AI in drug development holds promise, particularly in areas, such as personalized medicine, where AI enables tailored treatment approaches. As AI technologies continue to evolve, their integration into pharmaceutical research will revolutionize drug discovery and improve patient outcomes.