article · Informatics in Medicine Unlocked
Rising global cancer rates, therapy limitations, and drug resistance make the discovery of new oncology treatments essential. Traditional drug development remains slow, complex, and prone to high attrition rates during clinical trials. Computer-aided drug design addresses these challenges by forecasting the therapeutic efficacy of potential compounds and selecting the most viable candidates for subsequent testing. Widely used approaches include molecular docking, molecular dynamics simulations, quantitative structure-activity relationship modelling, and machine learning. These computational methodologies have helped identify small molecules that suppress tumour growth and spread via mechanisms such as cell cycle arrest, apoptosis, signal transduction inhibition, angiogenesis suppression, epigenetic modulation, and hedgehog pathway interference. Resolving the current constraints of these digital tools remains central to advancing efficacious anticancer candidates.
Cancer treatments frequently fail due to emerging drug resistance, while creating replacement therapies by conventional means takes decades and immense funding. Computational drug design accelerates the identification of viable molecules before costly laboratory testing. Understanding these digital tools enables researchers and developers to concentrate limited resources on therapeutic candidates with the greatest likelihood of overcoming resistance and succeeding in trials.
This work is relevant to early-stage pharmaceutical pipelines and computational biotechnology teams seeking to streamline lead discovery. Applying these virtual screening and modelling tools allows developers to refine target selection prior to in vitro testing. The findings represent early-stage computational research, serving as a methodological foundation for pipeline optimisation rather than near-market therapeutic products.
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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 anti-cancer 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 anti-cancer drug discovery. Computer-aided drug design (CADD), including molecular docking, molecular dynamics simulations, QSAR analysis, and machine learning, are employed to forecast 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.
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DOI: 10.1016/j.imu.2023.101332
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