Computational Approaches for Identifying Natural Multi-Target Drugs in Cancer Therapy- A Review Article
Keywords:
Computational drug, multi-target drug development, Cancer treatment, Natural compounds in drug discoveryAbstract
A new phase in drug discovery is essential. The majority of pharmaceutical development approaches rely on computer-generated data and insights. This article emphasizes advanced simulation techniques utilized in drug development. By the year 2040, the global incidence of cancer is expected to reach 30 million new cases, with the most significant increase occurring in low- and middle-income countries. Cancer diagnoses in the Americas are projected to rise by 55 percent, totaling 6.23 million cases by 2040. It is crucial to explore the kinetic profiles of ligand binding mechanisms and drug-target affinities. The review encompassed publications from August 2009 to August 2024 to incorporate both foundational and contemporary studies. Results & Discussion: Plant-derived pharmaceuticals encompass methyl transferase inhibitors, DNA damage inhibitors, HDAC inhibitors, and mitotic disruptors, focusing on their anticancer efficacy and development in clinical trials. Methyl transferase inhibitors (MTAs) are currently employed in cancer diagnostics as they target specific driver genes. However, this approach is limited by the extensive genetic alterations present in multiple driver genes within each tumor. The digital drug assignment (DDA) method prioritizes potential therapeutic candidates
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