Artificial Intelligence in Pharmaceutical Research and Development: A Comprehensive Review

Author

Bodake Ashvin Pandurang, Radhika S. Subhedar, Nilesh B. Chougule


Abstract

 The global pharmaceutical industry faces a productivity crisis marked by rising development costs, often estimated to exceed $2.6 billion per new molecular entity, and timelines that extend beyond a decade. Artificial intelligence (AI), including machine learning (ML), deep learning (DL) and generative AI, has emerged as a rational, data-driven alternative to the traditional trial-and-error approach. This review discusses the application of AI across the pharmaceutical value chain, covering drug discovery, preformulating, solid dosage form engineering, clinical trials, personalized medicine and manufacturing (Pharma 4.0). It also examines the regulatory landscape of biosimilars, highlighting the divergence in global standards and the role of analytical convergence in harmonizing approval pathways. Case studies such as the polymorphism of Nicergoline, the hydrolysis of Zidovudine and the generative design of microcrystalline cellulose networks illustrate how these tools are applied in practice. The challenges of model interpretability, data quality and regulatory acceptance, along with future directions such as federated learning and quantum computing, are also discussed.


Keywords

Artificial intelligence, machine learning, drug discovery, preformulation, biosimilars, Pharma 4.0.



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