ARCHIVES
VOL. 11, ISSUE 1 (2026)
Integrating artificial intelligence across the drug discovery pipeline: Applications, challenges, and future prospects in pharmaceutical sciences
Authors
Dr. C Pandian, Rathivadhana P, Reshma S, Sasi C, Navina S
Abstract
Artificial Intelligence (AI) is rapidly
transforming the pharmaceutical industry and has become a powerful catalyst for
modern drug discovery and development. AI approaches such as machine learning,
deep learning, natural language processing, and computer vision enable the
analysis of large-scale genomic, proteomic, chemical, clinical, and real-world
datasets that are beyond the capacity of conventional methods. AI accelerates
target identification, de novo drug design, lead optimization, ADME prediction,
toxicity assessment, and drug repurposing, thereby reducing cost, research
timelines, and experimental failure rates. AI models have shown major benefits
in rare disease diagnosis, personalized medicine and therapeutic target
validation, while AI-driven synthetic data generation further improves target
discovery when real sample availability is limited. AI-based quality control
enables real-time and predictive defect detection in pharmaceutical
manufacturing, minimizing waste and enhancing product reliability. Although
challenges remain related to data standardization, model transparency,
regulatory acceptance, ethics, and infrastructure requirements, AI holds strong
potential to reshape the drug development pipeline. This review highlights the
technological impact, evolving applications, advantages, limitations, and
future prospects of AI in pharmaceuticals, establishing AI as a central driver
toward faster, efficient, precise, and patient-centred drug discovery.
Pages:54-59
How to cite this article:
Dr. C Pandian, Rathivadhana P, Reshma S, Sasi C, Navina S "Integrating artificial intelligence across the drug discovery pipeline: Applications, challenges, and future prospects in pharmaceutical sciences". International Journal of Research in Pharmacy and
Pharmaceutical Sciences, Vol 11, Issue 1, 2026, Pages 54-59
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