Accelerating Drug Discovery: The Role of Generative AI and Big Data Analytics

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Published: Jun 19, 2025
DOI: https://doi.org/10.58812/esiscs.v3i01.606
Keywords:
Algorithms; Artificial Intelligence; Data Analytics; Drug Discovery; Machine Learning

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Ramchorn Gharami
Department of Pharmacy, Bangladesh University, Dhaka, Bangladesh
Delwar Karim
Independent Researcher, Dhaka, Bangladesh
Jhon Kabir
Department of Pharmacy, Bangladesh University, Dhaka, Bangladesh
Rashid Khan
Independent Researcher, Dhaka, Bangladesh

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.

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How to Cite
Gharami, R., Karim, D., Kabir, J., & Khan, R. (2025). Accelerating Drug Discovery: The Role of Generative AI and Big Data Analytics. The Eastasouth Journal of Information System and Computer Science, 3(01), 11–20. https://doi.org/10.58812/esiscs.v3i01.606
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Vol. 3 No. 01 (2025): Articles in press
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Articles
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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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