Cancer is considered one of the deadliest diseases globally, and continuous research is
being carried out to find novel potential therapies for myriad cancer types that affect the human
body. Researchers are hunting for innovative remedies to minimize the toxic effects of conventional
therapies being driven by cancer, which is emerging as pivotal causes of mortality worldwide.
Cancer progression steers the formation of heterogeneous behavior, including self-sustaining proliferation,
malignancy, and evasion of apoptosis, tissue invasion, and metastasis of cells inside the
tumor with distinct molecular features. The complexity of cancer therapeutics demands advanced
approaches to comprehend the underlying mechanisms and potential therapies. Precision medicine
and cancer therapies both rely on drug discovery. In vitro drug screening and in vivo animal trials
are the mainstays of traditional approaches for drug development; however, both techniques are laborious
and expensive. Omics data explosion in the last decade has made it possible to discover efficient
anti-cancer drugs via computational drug discovery approaches. Computational techniques
such as computer-aided drug design have become an essential drug discovery tool and a keystone
for novel drug development methods. In this review, we seek to provide an overview of computational
drug discovery procedures comprising the target sites prediction, drug discovery based on
structure and ligand-based design, quantitative structure-activity relationship (QSAR), molecular
docking calculations, and molecular dynamics simulations with a focus on cancer therapeutics.
The applications of artificial intelligence, databases, and computational tools in drug discovery
procedures, as well as successfully computationally designed drugs, have been discussed to highlight
the significance and recent trends in drug discovery against cancer. The current review describes
the advanced computer-aided drug design methods that would be helpful in the designing
of novel cancer therapies.