The use of computational tools in drug discovery has revolutionized the field of pharmaceutical research and development. Computer-Aided Drug Design (CADD) is a powerful approach that uses computational methods to identify and optimize potential drug candidates. However, as the complexity of drug targets increases, the challenges associated with CADD also grow. This review highlights the emerging problems and opportunities in CADD in the 21st century. We discuss the current state-of-the-art methods in CADD, including machine learning, data mining, and molecular dynamics simulations. We also identify the key challenges facing CADD, including the need for more accurate models and the integration of multi-omics data. Finally, we explore the opportunities presented by new technologies, such as quantum computing and artificial intelligence, and their potential impact on the future of CADD