ADVANCEMENTS IN COMPUTATIONAL DRUG DESIGN AND NATURAL THERAPEUTICS: A SYSTEMATIC REVIEW OF EMERGING STRATEGIES
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Objective: The growing prevalence of chronic diseases, including cancer, and the rise of drug resistance highlight the urgent need for novel, effective, and safer therapeutic strategies. There is an abounding history of the use of natural products, which are derived from plants, fungi, and microorganisms. Modern tools, such as computational drug design, in silicon modeling and high-throughput screening have been integrated with natural products for drug discovery in recent years. Methods: This review discusses the juxtaposition of natural products towards modern drug discovery with specific interest to cancer and other diseases. An overview of computational drug design strategies, such as molecular docking, time-domain simulations, and quantitative structure-activity relationship (QSAR) applied to natural compounds is revived. Additionally, it discusses high-throughput screening and bioinformatics methods that can aid in the identification and optimization of natural drug candidates. Results: The combined implementation with computational methods and natural products is speeding up the identification of potential drug candidates, with increased bioavailability and less toxicity. Moreover, the integration of personalized medicine and nanotechnology in this domain has been shown to improve their therapeutic potential and biodistribution, providing more effective and tailored treatment options for diseases such as cancer and cardiovascular disorders also. Novelty: The plant origin of the compound and its traditional therapeutic use make it an interesting alternative coupled to modern technologies of drug discovery. Through overcoming challenges related to bioavailability and toxicity; while integrating advanced techniques such as nanotechnology and personalized medicine.
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