基于TOPSIS的多标准QSPR抗生素建模,使用图形理论指数
Atef F Hashem1, Muhammad Farhan Hanif2, Amber Shafiq3
1Department of Mathematics and Statistics, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia.
Scientific reports
|November 19, 2025
概括
这项研究使用图形理论和M多项式来分析抗生素结构,创建药物特性预测模型. 这些模型有助于选和优化药物发现的抗生素.
科学领域:
- 药用化学 医学化学
- 计算化学计算化学
- 化学信息学 化学信息学
背景情况:
- 抗生素药物发现依赖于理解结构-性质关系.
- 拓描述符为预测建模提供了一种量化分子结构的方法.
研究的目的:
- 通过拓描述器研究抗生素药物的结构特征.
- 开发定量结构属性关系 (QSPR) 模型,用于预测抗生素特性.
- 使用计算描述符和决策方法对抗生素进行排名.
主要方法:
- 利用M多项式框架来定义拓描述符.
- 基于度的计算指数 (例如,萨格勒布,波,被遗忘).
- 用于QSPR的立方体和功率回归模型,以及用于多标准决策的TOPSIS和SAW.
- 为了客观特征的重要性,应用了权.
主要成果:
- 立方回归模型显示QSPR.的预测精度更高 (较大的确定系数,较低的标准误差).
- 综合的QSPR,图形理论和决策分析有效地对抗生素进行了排名.
- 权衡方案确保了客观特征的重要性.
结论:
- QSPR建模,图形理论指数和基于的决策分析的综合方法对抗生素查具有强大作用.
- 这种方法为抗生素药物发现和优化提供了宝贵的见解.
- 来自M多项式框架的拓描述符是有效的分子描述符.
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