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The integration of artificial intelligence (AI) in drug discovery has revolutionized the field, offering unprecedented opportunities for accelerating the development of novel therapeutics. AI's adaptability and predictive capabilities have been successfully applied across various stages of the drug discovery process, including target identification, compound screening, and lead optimization. By leveraging machine learning algorithms and big data, researchers can expedite the discovery of promising compounds, reduce human workload, and improve the quality of life. This review provides a comprehensive overview of AI's role in drug discovery, highlighting its applications, advantages, and challenges. The current state of AI in drug discovery, its potential to transform the field, and the limitations that need to be addressed. Furthermore, explore the future directions of AI in drug discovery, including the need for high-quality data, standardization, and regulatory acceptance.
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Rotake et al. (2025) studied this question.
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