Abstract

ARTIFICIAL INTELLIGENCE (MACHINE LEARNING) IN PHARMACEUTICALS: NAVIGATING REGULATORY EXPECTATIONS AND TECHNICAL ADVANCEMENTS FOR DRUG PRODUCT

Krupalkumar Morker*

ABSTRACT

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into pharmaceutical research and development represents a transformative shift from traditional, resource-intensive drug discovery paradigms. This review comprehensively examines the strategic opportunities and challenges presented by AI/ML across the drug development lifecycle. From target identification and virtual screening to de novo drug design, chemical synthesis, and clinical trial optimization, AI technologies are enhancing precision, accelerating timelines, and reducing costs. We discuss key AI models—including generative adversarial networks, graph neural networks, and transformers—and their applications in drug formulation, toxicity prediction, pharmacovigilance, and personalized medicine. Despite its promise, the adoption of AI faces significant hurdles, including data quality, model interpretability, ethical concerns, and evolving regulatory landscapes. The U.S. Food and Drug Administration has begun issuing frameworks to guide the responsible integration of AI in drug development, emphasizing transparency, validation, and bias mitigation. Looking forward, the convergence of AI with automation and real-world data holds the potential to realize fully autonomous laboratories and more equitable, efficient global healthcare solutions. This article underscores AI’s role not merely as a tool, but as a catalyst for a new era of intelligent, patient-centric pharmaceutical innovation.

Keywords: Artificial Intelligence, Machine Learning, Drug Discovery, Pharmaceutical Development, AI in Clinical Trials, Personalized Medicine, Drug Repurposing, Pharmacovigilance, Regulatory Science, De Novo Drug Design, Virtual Screening, Pharmaceutical Analysis,