The pharmaceutical industry faces unprecedented challenges including rising development costs, high clinical trial failure rates, and increasing pressure to deliver faster, safer, and more effective therapeutics. In response, the integration of generative artificial intelligence (AI) and big data analytics has emerged as a transformative approach to drug discovery. Generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and transformer-based architectures are revolutionizing the early phases of drug development by enabling de novo molecule generation, protein structure prediction, and optimization of pharmacokinetic properties. Meanwhile, predictive analytics powered by machine learning (ML) and deep learning (DL) techniques are enhancing compound screening, target identification, and clinical trial simulation.

This review article explores the convergence of generative AI and big data in pharmaceutical research, detailing their synergistic role in expediting drug discovery pipelines. It provides a comprehensive overview of current methodologies, discusses case studies of AI-driven discoveries, and evaluates the technological infrastructure required to operationalize these advancements. The paper also addresses challenges such as data privacy, model explainability, and validation, while highlighting future trends including quantum AI, multimodal learning, and AI-driven personalized medicine. Ultimately, this review demonstrates how generative AI, when fused with robust data ecosystems, holds the potential to radically transform pharmaceutical innovation.