Abstract

The integration of deep learning (DL) in drug discovery is revolutionizing pharmaceutical research by accelerating the identification of drug candidates, predicting drug-target interactions, and optimizing molecular properties. This paper explores how DL architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs) are reshaping drug discovery pipelines. We discuss the application of DL across various stages—target identification, compound screening, and de novo drug design— supported by case studies and performance comparisons. Additionally, we highlight the challenges of data quality, model interpretability, and regulatory integration in real-world pharmaceutical settings.