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Article Type

Review

Abstract

Artificial intelligence (AI) has emerged as a transformative technological paradigm within the pharmaceutical sciences, offering innovative and forward-looking solutions to surmount the inherent limitations associated with conventional drug development, formulation design, and therapeutic delivery systems. Traditional pharmaceutical methodologies frequently entail protracted development timelines, extensive experimental procedures, and substantial financial expenditures, particularly within the domains of formulation optimization and drug delivery design. The integration of AI-driven technologies – encompassing machine learning (ML), deep learning (DL), artificial neural networks (ANNs), and predictive analytics – has considerably accelerated pharmaceutical research by refining decision-making processes, alleviating experimental burden, and enhancing formulation efficiency. The present review delineates the pivotal role of AI in optimizing pharmaceutical formulation through its applications in high-throughput screening, physicochemical property prediction, excipient compatibility assessment, stability forecasting, nanoparticle optimization, and Quality by Design (QbD) frameworks. Furthermore, the article examines AI-driven advancements in drug delivery systems, encompassing personalized medicine, controlled-release formulations, targeted delivery platforms, pharmacokinetic-pharmacodynamic modeling, and real-time process monitoring technologies. The consequential impact of AI upon pharmaceutical manufacturing, quality control, drug discovery, ADMET prediction, generative drug design, and drug repurposing strategies is likewise critically examined. In addition, regulatory considerations, data integrity challenges, explainable AI frameworks, and emerging disruptive technologies – such as quantum computing – are comprehensively explored as prospective future directions. Collectively, AI-driven methodologies are fundamentally reshaping the pharmaceutical development landscape by augmenting efficiency, precision, and therapeutic outcomes, while concurrently supporting the transition toward personalized and evidence-based medicinal practice.

Keywords

Artificial intelligence, Machine learning, Pharmaceutical formulation, Drug delivery systems, Drug development, Quality by design, Personalized medicine, Pharmaceutical manufacturing, Predictive modeling, Drug discovery

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