使用拓指数和回归建模预测黄类物质的物理化学性质
Huili Li1,2, Shamaila Yousaf3, Komal Shahzadi3
1School of Software, Pingdingshan University, Pingdingshan, 467000, China.
Scientific reports
|July 29, 2025
概括
这项研究使用拓指数来预测黄类物质的性质,发现二次模型最适合估计摩尔折射率,摩尔体积和蒸发度. 这有助于通过优先考虑生物活性化合物来实现具有成本效益的药物发现.
科学领域:
- 化学信息学和定量结构与属性关系 (QSPR) 研究.
- 植物化学分析和药物发现.
- 计算化学和分子建模.
背景情况:
- 黄酸是具有多种生物活动的重要多基植物化学物质.
- 预测物理化学性质对于理解和利用这些化合物至关重要.
- 拓指数为分子表征提供了一个计算方法.
研究的目的:
- 使用基于度的拓指数,预测60种黄类的6种物理化学性质.
- 评估用于属性预测的线性,二次性和对数回归模型的性能.
- 在药物发现中建立一个具有成本效益的方法来优先考虑生物活性黄类药物.
主要方法:
- 利用基于度的拓索引 (TI) 来表示分子结构.
- 用线性,二次性和对数回归模型进行预测.
- 使用相关系数 (R2),根平均平方误差 (RMSE) 和平均绝对误差 (MAE) 验证模型性能.
主要成果:
- 二次回归模型证明了对摩尔折射率,摩尔体积和蒸发度的优越预测能力.
- 对外部化合物的预测值和实验值之间观察到很高的统计一致性.
- 在拓指数和研究的物理化学性质之间确定了非线性关系.
结论:
- 开发的QSPR方法提供了一种可靠且具有成本效益的工具,用于快速估计类黄的性质.
- 这种方法有助于在药物发现管道中优先考虑有前途的黄类候选物.
- 该研究强调了拓索引在桥梁化学信息学和多系统的生物应用中的实用性.
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