AI-Powered Drug Discovery: Accelerating the Identification of Novel Therapeutics

Abstract

Traditional drug discovery is a time-consuming and expensive process. This study investigates how AI-driven techniques, including generative adversarial networks (GANs) and reinforcement learning, can accelerate drug discovery by predicting molecular interactions, optimizing compound synthesis, and repurposing existing drugs. By training models on vast biochemical datasets, our system identifies promising drug candidates for diseases such as cancer and neurodegenerative disorders. The results showcase AI’s transformative impact on pharmaceutical research, reducing development time and improving success rates.