Artificial Intelligence and Machine Learning in Pharmaceutical Chemistry: Transforming Drug Design, Optimization, and Development
Keywords:
Artificial intelligence, Machine learning, Drug discovery, Pharmaceutical chemistry, Computational drug design, Data-driven modellingAbstract
Artificial intelligence (AI) and machine learning (ML) have emerged as transformative technologies in pharmaceutical chemistry, reshaping traditional approaches to drug discovery and development. Conventional drug development is time-consuming, costly, and characterized by high attrition rates. AI-driven computational models offer powerful tools for accelerating drug design, optimizing lead compounds, predicting physicochemical and biological properties, and improving decision-making across the drug development pipeline. This review discusses the fundamental concepts of AI and ML relevant to pharmaceutical chemistry and highlights their applications in target identification, de novo drug design, structure–activity relationship modeling, synthesis planning, and process optimization. Challenges related to data quality, model interpretability, and regulatory acceptance is also examined. Future prospects emphasize the integration of AI with experimental chemistry and automation to enable faster, more efficient and cost-effective pharmaceutical innovation.
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