Abstract

Drug discovery is traditionally slow and costly, but machine learning is revolutionizing this process by enabling efficient exploration of chemical space and accurate prediction of drug properties. This review highlights key Machine learning technologies especially deep generative models and their integration with molecular simulations to accelerate drug design. We discuss applications such as virtual screening, de novo molecule generation, and ADMET prediction, while addressing challenges like data limitations and model interpretability. Finally, we outline future directions including multi modal learning, reinforcement learning for synthesis planning, explainable AI, federated learning, and quantum machine learning, emphasizing their potential to transform drug discovery.