Computer-aided drug design in anti-cancer drug discovery: What have we learnt and what is the way forward?

Abstract

The escalating prevalence of cancer on a global scale, coupled with the inadequacies of present-day therapies and the emergence of drug-resistant cancer strains, has necessitated the development of additional anticancer drugs. The traditional drug discovery process is long and complex, and the high failure rate of new drugs in clinical trials further highlights the need for computational approaches in anticancer drug discovery. Computer-aided drug design (CADD), including molecular docking, molecular dynamics simulations, QSAR analysis, and machine learning, are employed to predict the efficacy of potential drug compounds and pinpoint the most auspicious compounds for subsequent testing and advancement. This article provides an overview of contemporary computational approaches employed in the design of anti-cancer drugs. It highlights a range of small molecules that have been identified as capable of impeding cancer growth and migration through various mechanisms, including cell cycle arrest/apoptosis, signal transduction inhibition, angiogenesis, epigenetics, and the hedgehog pathway. It also examines the constraints of computational techniques and presents remedies to surmount these limitations in the development and identification of efficacious anticancer compounds.