黄和酸的交叉数据库表征:整合药物相似度指标,分子相互作用和饮食来源
Christmas Maria Vidal de Barros Rêgo1, Zafirah Muhammad Rahman2, Anna Paula Aguiar1
1Center of Innovation, Technology, and Education (CITE) at Anhembi Morumbi University-Anima Institute, Sao Jose dos Campos Technology Park, Sao Jose dos Campos 12247-016, Brazil.
Molecules (Basel, Switzerland)
|February 27, 2026
概括
这项研究整合了计算指标,分子相互作用和黄和酸的饮食数据. 它揭示了异黄具有很高的药物相似性,而黄类药物显示出更大的蛋白质结合,草药,水果和茶是丰富的来源.
科学领域:
- 计算化学和药理学计算化学和药理学
- 营养科学 营养科学
- 生物信息学是一种生物信息学.
背景情况:
- 黄和酸具有治疗潜力,但由于可变的生物可用性和碎片化的数据,临床转化面临挑战.
- 需要一个统一的框架来系统地评估这些化合物的药物相似性,分子相互作用和饮食来源.
研究的目的:
- 通过计算指标,分子相互作用数据和饮食来源对黄和酸进行系统,综合性分析.
- 为这些化合物建立一个全面的表征框架,以帮助药物开发和营养研究.
主要方法:
- 来自PhytoHub,Phenol-Explorer,ChEMBL和FoodDB的综合数据,涵盖954种化合物 (715种黄,239种酸).
- 使用药物相似性的定量估计 (QED) 和DataWarrior分数评估药物相似性.
- 通过ChEMBL活动数据分析了分子相互作用,并使用FoodDB绘制了食物来源.
主要成果:
- 药物相似性得分 (QED和DataWarrior) 显示了互补的模式,异黄呈现最有利的概况 (平均QED:0.62±0.18).
- 黄类药物表现出明显更高的结合亲缘关系 (平均ChEMBL得分:7.26±1.09) 和向更多的蛋白质 (67) 比酸 (33).
- 草药,香料,水果和茶被确定为最丰富的饮食来源,具有明显的化合物分布模式.
结论:
- 综合性,跨数据库的方法为表征黄和酸提供了一个强大的框架.
- 这种方法系统地评估药物相似性,分子相互作用和饮食来源,支持药物开发和营养应用的化合物选择.
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