Generative AI in drug discovery: Accelerating the search for new therapeutics

Potineni, Bhavyateja (2025) Generative AI in drug discovery: Accelerating the search for new therapeutics. World Journal of Advanced Engineering Technology and Sciences, 15 (1). pp. 1784-1794. ISSN 2582-8266

Generative AI is revolutionizing drug discovery by drastically shortening the traditionally lengthy and costly development process. By leveraging advanced machine learning techniques like Variational Autoencoders, Generative Adversarial Networks, and reinforcement learning, AI systems can design novel therapeutic molecules with desired properties before synthesis occurs in the lab. These technologies enable pharmaceutical researchers to efficiently navigate the vast chemical space of potential drugs, simultaneously optimize for multiple molecular properties, create entirely new chemical structures, repurpose existing medications, and potentially reduce clinical failure rates. Integrating AI approaches with traditional drug discovery methods promises to accelerate innovation in therapeutics, particularly for diseases with significant unmet medical needs. It may fundamentally transform how new medicines reach patients in need.