Cancer is considered as one of the world's leading causes of morbidity and mortality. Over the past four decades, spectacular advances in molecular and cellular biology have led to major breakthroughs in the field of cancer research. However, the design and development of anticancer drugs prove to be an intricate, expensive, and time-consuming process. To overcome these limitations and manage large amounts of emerging data, computer- aided drug discovery/design (CADD) methods have been developed. Computational methods can be employed to help and design experiments, and more importantly, elucidate structure-activity relationships to drive drug discovery and lead optimization methods. Structure- and ligand-based drug designs are the most popular methods utilized in CADD. Additionally, the assimilation provided by these two complementary approaches are even more intriguing. Nowadays, the integration of experimental and computational approaches holds great promise in the rapid discovery of novel anticancer therapeutics. In this review, we aim to provide a comprehensive view on the state-of-the-art technologies for computer-assisted anticancer drug development with thriving models from literature. The limitations associated with each traditional in silico method have also been discussed, which can help the reader to rationale the best computational tool for their analysis. In addition, we will also shed some light on the latest advances in the computational approaches for anticancer drug development and conclude with a brief precis.
- Drug repositioning