Federal University of Paraíba, Centre for Biotechnology, 50670-910, João Pessoa, PB, Brazil.
Curr Top Med Chem. 2012;12(24):2785-809. doi: 10.2174/1568026611212240007.
Flavonoids are phenolic compounds, secondary metabolites of plants that cause several benefits to our health, including helping the treatment against cancer. These pharmacological properties are associated with the ability of flavonoids in attenuating the generation of reactive oxygen species, acting as chelate compounds or affecting the oxi-redox cycle. In spite of the large number of information in term of SAR and QSAR, no recent review has tabulated and discussed in detail these data. In view of this, we bring here a detailed discussion of the structure-activity relationships (SAR) and quantitative structure-activity relationships (QSAR) models. We have also analyzed the correlation between the chemical structure of flavonoids and analogues to their anticancer activities. A large number of methodologies have been used to identify the characteristics of these compounds with their potential anticancer: multiple linear regression, principal components analysis, comparative molecular field analysis, comparative molecular similarity indices analysis, partial least squares, neural networks, configuration of classification and regression trees, Free-Wilson, docking; using topological, structural and enthalpies' descriptors. We also discussed the use of docking models, together with QSAR models, for the virtual screening of anticancer flavonoids. The importance of docking models to the medicinal chemistry of anticancer flavonoids has increased in the last decade, especially to help in identifying the structural determinants responsible for the activity. We tabulated here the most important examples of virtual screening determined for anticancer flavonoids and we highlighted the structural determinants. The mode of action, the most potent anticancer flavonoids and hints for the structural design of anticancer flavonoids are revised in details and provided here.
类黄酮是酚类化合物，是植物的次生代谢产物，对我们的健康有多种益处，包括帮助治疗癌症。这些药理特性与类黄酮减轻活性氧生成的能力有关，类黄酮可以作为螯合物或影响氧化还原循环。尽管在 SAR 和 QSAR 方面有大量的信息，但最近没有一篇综述详细列出和讨论这些数据。有鉴于此，我们在这里详细讨论了结构活性关系 (SAR) 和定量结构活性关系 (QSAR) 模型。我们还分析了类黄酮及其类似物的化学结构与抗癌活性之间的相关性。已经使用了大量的方法来确定这些化合物的特性及其潜在的抗癌活性：多元线性回归、主成分分析、比较分子场分析、比较分子相似性指数分析、偏最小二乘、神经网络、分类和回归树的配置、Free-Wilson、对接；使用拓扑、结构和焓描述符。我们还讨论了对接模型与 QSAR 模型一起用于抗癌类黄酮的虚拟筛选。在过去十年中，对接模型对抗癌类黄酮的药物化学的重要性有所增加，特别是有助于确定负责活性的结构决定因素。我们在这里列出了用于抗癌类黄酮的虚拟筛选的最重要示例，并突出了结构决定因素。作用方式、最有效的抗癌类黄酮以及抗癌类黄酮的结构设计提示都进行了详细的回顾并提供在这里。
