基于机器学习的QSPR分析使用回归模型对安非他胺衍生物进行分析
Muhammad Farhan Hanif1, Atef F Hashem2, Mazhar Hussain1
1Department of Mathematics and Statistics, The University of Lahore, Lahore Campus, Lahore, Pakistan.
Scientific reports
|January 14, 2026
概括
胺衍生物的定量结构属性关系 (QSPR) 模型是使用基于邻近度的拓索引和NM多项式开发的. 这些模型有效地预测物理化学性质,帮助药物设计和选.
科学领域:
- * 化学信息学
- * 计算化学 计算机化学
- * 医学化学 医学化学
背景情况:
- * 了解分子结构和物理化学性质之间的关系对于药物发现至关重要.
- *拓索引和多项式回归为QSPR建模提供了潜力.
- * 胺衍生物是一种具有显著药理意义的化合物.
研究的目的:
- *为安非他胺衍生物建立定量结构-属性关系 (QSPR).
- * 评估基于邻近度的拓索引和NM多项式的预测能力.
- * 将多项式回归模型与属性预测的随机森林算法进行比较.
主要方法:
- * 基于邻近度的拓索引和胺衍生物的NM多项式的计算.
- * 开发多项式回归模型 (立方和二次) 和随机森林算法.
- * 预测物理化学性质,包括沸点,蒸发能量,闪点,摩尔折射率,表面张力,极化性和SA (表面积).
主要成果:
- *基于社区的指数有效地捕捉了与刺激行为相关的结构复杂性,连接性和电子特征.
- *立方回归模型与二次模型相比,表现出更好的非线性结构关系的能力.
- *随机森林算法显著提高了预测准确性和通用性,特别是对于依赖分子分支和电子分布的属性.
结论:
- * 基于NM多项式的描述符成功地将分子拓与可测量的物理化学性质相关联.
- *开发的QSPR模型对于计算性质预测,早期药物查和化学信息学驱动的分子设计有价值.
- *这种方法为理解和预测刺激剂类型分子的行为提供了一个强大的框架.
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