THE AI REVOLUTION IN PHARMACEUTICALS: INNOVATIONS, CHALLENGES, AND FUTURE PROSPECTS – AN OVERVIEW
DOI:
https://doi.org/10.22159/ijap.2026v18i1.55744Keywords:
Artificial intelligence, Clinical trials, Drug discovery, Formulation, Machine learning, PharmaceuticalsAbstract
Artificial intelligence (AI) is transforming pharmaceutical research and development (R and D), and making measurable improvements in efficiency, precision, and cost-effectiveness in drug research and development. AI-enabled platforms have cut the drug discovery pipeline timelines in comparison to the traditional 4-6 y down to 46 d, along with speeding up compound screening by 1-2 y and reduced clinical trial duration by up to 59% and increased the accuracy of patient selection 80-90%. In formulation optimization artificial neural networks, neuro--fuzzy systems, and hybrid model-based AI models have been able to predict dissolution profile and critical quality attributes with accuracy rates of over 90%, with 30-50% lower experimental workload. In this review, the cross-domain evidence on the use of AI in the continuum of target identification to regulatory integration is thoroughly synthesized and critical evaluations on existing limitations which include data bias, interpretability discrepancy and regulatory ambiguity discussed. It proposes a systematized framework of integration, which places the emphasis on creating high impact pilot projects, in-the-wild testing and further monitoring or observing of models according to the instructions of FDA, EMA and EU AI Act. Synthesizing measures of quantitative values along with practical measures, the present work offers a blueprint of unambiguously converting the ideological potential of AI into implementable, regulator-compatible utilities in pharmaceutical science.
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