Transforming Cancer Treatment: A Thorough Investigation of Computer-Aided Drug Design in the Development of Anti-Cancer Agents

Abstract

In today's landscape, cancer stands as a formidable global health challenge. Despite substantial strides in cancer research, propelled by breakthroughs in molecular and cellular biology, the journey to devise effective anticancer medications remains intricate, resource-intensive, and time-consuming[1,2]. To address these issues and deal with the increasing amount of data, the field of computer-aided drug discovery/design (CADD) was created. The demand for more anticancer drugs has grown due to the increasing global cancer rates, limitations of current treatments, and the emergence of drug-resistant cancer forms[3]. Molecular docking, molecular dynamics simulations, QSAR analysis, and machine learning are all integral parts of computer-aided drug design (CADD), crucial for predicting the efficacy of new therapeutic compounds and selecting the most promising ones for further research and development[4]. This article offers an overview of modern computational techniques in anti-cancer drug development, showcasing various small molecules proven effective in impeding cancer growth and spread through mechanisms like angiogenesis inhibition, signal transduction blocking, cell cycle arrest/apoptosis, epigenetics, and modulation of the hedgehog pathway. It also discusses the limitations of computational methods and proposes solutions for their use in crafting potent anticancer medications.