Year: 2025
Type: article
Abstract: Drug discovery has long been characterized by extensive timelines, high costs, and significant risks, often taking more than a decade and billions of dollars to bring a single drug to market. However, the convergence of generative artificial intelligence (AI) and big data analytics is fundamentally reshaping this landscape. This paper provides an in-depth analysis of generative AI especially models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and transformer-based architectures combined with vast biological and chemical datasets, is transforming molecular design, target identification, and compound optimization. Through a systematic review of literature, comparative model evaluation, and real-world case studies including AlphaFold, the paper explores the efficacy of these technologies in accelerating drug discovery. A hybrid methodology combining data mining, model testing, and bioinformatics simulation is employed. The results demonstrate significant improvements in candidate molecule generation, predictive modeling accuracy, and time-to-market for new drugs. Future challenges such as data interoperability, ethical considerations, and regulatory compliance are also discussed. The study concludes by highlighting the immense potential of AI and big data in ushering a new era of precision medicine and personalized therapeutics.
Authors: Ramchorn Gharami, Delwar Karim, Jhon Kabir, Rashid Khan
Institution: Bangladesh University
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4411460268