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Prediction of Molecular Targets of Cancer Preventing Flavonoid Compounds Using Computational Methods

机译:用计算方法预测预防类黄酮化合物的分子靶标

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摘要

Plant-based polyphenols (i.e., phytochemicals) have been used as treatments for human ailments for centuries. The mechanisms of action of these plant-derived compounds are now a major area of investigation. Thousands of phytochemicals have been isolated, and a large number of them have shown protective activities or effects in different disease models. Using conventional approaches to select the best single or group of best chemicals for studying the effectiveness in treating or preventing disease is extremely challenging. We have developed and used computational-based methodologies that provide efficient and inexpensive tools to gain further understanding of the anticancer and therapeutic effects exerted by phytochemicals. Computational methods involving virtual screening, shape and pharmacophore analysis and molecular docking have been used to select chemicals that target a particular protein or enzyme and to determine potential protein targets for well-characterized as well as for novel phytochemicals.
机译:基于植物的多酚(即植物化学物质)已经用作人类疾病的治疗已有数百年历史了。这些植物来源的化合物的作用机理现在是研究的主要领域。已分离出数千种植物化学物质,其中许多已在不同疾病模型中显示出保护活性或作用。使用常规方法选择最佳的单一或一组最佳化学物质来研究治疗或预防疾病的有效性极具挑战性。我们已经开发并使用了基于计算的方法,这些方法可提供有效且廉价的工具,以进一步了解植物化学物质产生的抗癌和治疗作用。已经使用涉及虚拟筛选,形状和药效团分析以及分子对接的计算方法来选择靶向特定蛋白质或酶的化学物质,并确定特征明确的以及新型植物化学物质的潜在蛋白质靶标。

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