冠状动脉疾病药物的QSPR研究,使用基于偏心率的指数
Naveed Iqbal1, Shehnaz Akhter2, Alhafez M Alraih3
1Department of Mathematics, College of Science, University of Ha'il, Ha'il, 2440, Saudi Arabia.
Scientific reports
|November 11, 2025
概括
这项研究增强了使用新型图形不变的冠状动脉病药物的定量结构-属性关系 (QSPR) 建模. 基于偏心率的指数改善了关键物理化学性质的预测,验证了模型的有效性.
科学领域:
- 计算化学和化学信息学.
- 药物化学和药物发现.
- 心血管药理学. 心血管药理学.
背景情况:
- 冠状动脉疾病 (CAD) 是全球主要的死亡和发病原因.
- 准确预测药物的物理化学性质对于有效的CAD治疗至关重要.
- 定量结构与属性关系 (QSPR) 建模将化学结构与属性联系起来.
研究的目的:
- 使用基于偏心的图形不变量来推进CAD药物的QSPR建模.
- 为了提高CAD治疗剂的物理化学性质的预测准确度.
- 探索结构-属性关系的线性和非线性回归模型.
主要方法:
- 从分子图表计算基于异心率的图形不变量.
- 开发使用线性,对数,二次和立方回归的QSPR模型.
- 独立变量:以离心率为基础的不变量;依赖变量:物理化学性质 (沸点,蒸发度等). ) 的情况.
- 使用独立的一组CAD药物的验证.
主要成果:
- 非线性模型表现出药物性质的优异预测性能.
- 奇特的阿尔伯森和奇特的几何算术指数显示出高的预测能力.
- 立方回归优化了对蒸发的度,摩尔折射率,极化性和复杂性的预测.
- 二次回归最好预测分子重量;线性回归预测重原子数.
- 逻辑回归准确建模了沸点和摩尔体积.
结论:
- 基于偏心的图形不变量显著提高了CAD药物的QSPR模型准确性.
- 非线性回归模型,特别是立方模型,对于预测几个关键的物理化学性质是最佳的.
- 开发的QSPR模型对于评估重要药物参数是强大而可靠的.
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