Introduction: Cancer is a major global health concern, causing millions of deaths each year due to the uncontrolled growth and spread of abnormal cells. Despite advances in diagnosis and treatment, its complexity and therapy resistance keep it a leading cause of mortality. This review highlights the impact of CADD and related computational approaches in advancing cancer research and therapy. Methods: This literature review examined studies on CADD in cancer research, focusing on its use in drug discovery, target identification, and compound optimization. It describes how CADD accelerates drug development, reduces costs, and supports the design of more selective anticancer agents. Discussion: Traditional experimental methods are time-consuming, costly, and limited in predictive power. Computational innovations such as CADD, AI, ML, and quantum computing enable rational drug design, predictive modeling, and efficient target prioritization. Early-stage challenges illustrated by PROTACs, EGFR inhibitors, miRNA therapeutics, and T-cell-engaging bispecific antibodies include issues related to selectivity, toxicity, drug resistance, and limitations of preclinical models. Although tumor biomarkers aid detection and monitoring, sensitivity and reproducibility remain concerns. AI, ML, and quantum computing enhance screening, toxicity prediction, and the simulation of protein-ligand interactions. Despite regulatory and clinical hurdles, integrating genomics, proteomics, imaging, and computational approaches offers a path toward more effective, personalized cancer therapies. Results: In this review study, it was found that computer-aided drug design and related computational approaches have significantly enhanced cancer drug discovery. Several methods including molecular docking, molecular dynamics, QSAR, virtual screening and AI/ML driven models have accelerated target identification, lead optimization and prediction of efficacy, toxicity and resistance. In multiple instances, integration of multi-omics data has improved biomarker discovery and patient stratification. Several case studies on EGFR, PARP, KRAS G12C and immunotherapy targets have demonstrated reduced development time and improved selectivity. Overall, computational innovations have streamlined anticancer drug development while providing substantial leads for experimental validation. Conclusion: CADD is transforming cancer research by accelerating drug discovery, optimizing target interactions, and aiding the design of selective agents. Its limitations include reliance on structural data, high computational demands, and the need for experimental validation. Future efforts should integrate AI, machine learning, and multi-omics data to improve predictive accuracy and accelerate therapy development.
Lytan et al. (2026) studied this question.