使用对优先级多环芳的回归模型对索博尔指数及其各种版本进行比较研究
B Kirana1,2, M C Shanmukha3,4, A Usha5
1Department of Mathematics, KVG College of Engineering, Sullia, 574327, India.
Scientific reports
|August 27, 2024
概括
本研究评估了用于预测多环芳 (PAH) 属性的拓指数. 索博尔指数 (SO) 和逆索博尔指数 (RSO) 显示出这些重要的芳香化合物的强大预测潜力.
科学领域:
- 计算化学的计算化学
- 化学信息学 化学信息学
- 材料科学 材料科学 材料科学
背景情况:
- 多环芳 (PAH) 是具有化环的芳化合物,在各种科学领域至关重要.
- 拓指数为PAHs的结构-属性关系提供了有价值的见解.
- 应用范围包括计算化学,药物设计和定量结构与属性关系 (QSPR) 研究.
研究的目的:
- 分析38个优先级的PAHs的几个Sombor型拓指数的预测潜力.
- 为了评估索博尔指数 (SO),圆索博尔指数 (ESO),欧勒索博尔指数 (EU),反向索博尔指数 (RSO),反向圆索博尔指数 (RESO) 和反向欧勒索博尔指数 (REU).
- 利用回归模型来评估这些指数的预测能力.
主要方法:
- 应用回归模型 (线性,二次性,立方) 来预测PAH特性.
- 专注于以度为基础的和以逆度为基础的拓指数.
- 对38种高优先级的多环芳的分析.
- 评估根平均平方误差 (RMSE) 以确定预测准确度.
主要成果:
- 索博尔指数 (SO) 和逆索博尔指数 (RSO) 成为测试中最有效的预测指数.
- 回归模型显示了所选指数的不同程度的预测能力.
- 为不同的回归模型生成了图形,说明最佳预测指数与最小RMSE的变化.
结论:
- SO和RSO被认为是预测多环芳的特性非常有前途的拓指数.
- 这项研究为利用这些指数在计算化学和QSPR分析中提供了基础.
- 进一步的研究可以利用这些发现,在药物设计和材料科学中进行更准确的属性预测.
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