Abstract

Artificial Intelligence (AI) is emerging as a transformational technique in pharmaceutical research and development, enabling the shift from conventional experimental approaches to predictive, data-driven methodologies. This review is a comprehensive overview of the use of AI in the drug development pipeline, such as identifying targets, discovering hits, lead optimization, preclinical trials, clinical trials, and post-marketing surveillance. Machine learning, deep learning, natural language processing, and generative models are advanced methods that assist the integration of data on a multi-dimensional basis and make decisions about data DSSs more efficient. Despite the challenges related to data quality, interpretability, and regulatory compliance, AI demonstrates significant potential in improving translational success and accelerating therapeutic innovation towards precision and patient centric healthcare.

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Data Sharing Statement

Funding