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使用Sombor分子描述器对粉素进行数据驱动回归分析

Zeeshan Saleem Mufti¹, Muhammad Asim¹, A S Shflot²

¹Department of Mathematics and Statistics, The University of Lahore, Lahore, Pakistan.

Scientific reports

|December 22, 2025

概括

这项研究引入了基于面积的新型Sombor指数,用于分析粉素结构. 索博尔索引SO和SO有效地预测了粉素的特性,推进了多糖的分子建模.

科学领域:

拓化学和分子建模.
生物化学和多糖类分析.

背景情况:

粉中的关键成分粉糖对于植物的能量储存至关重要,并具有营养影响.
拓分子分析为分子结构和性质提供了洞察力.

研究的目的:

用新型的Sombor指数分析粉素结构.
探索拓指数和物理化学性质之间的关系.
为了确定最有效的Sombor指数来预测粉素的行为.

主要方法:

应用欧几里德几何来导出以面积为基础的,以周长为基础的和以角为基础的索博尔索引.
使用监督机器学习进行回归分析,以将索引与属性相关联.
差异分析 (ANOVA) 用于确定统计学意义.

主要成果:

引入了第一个基于面积的Sombor指数.
鉴定SO[公式:见文本]和SO[公式:见文本]作为粉素属性的强有力的预测剂.
SO[公式:参见文本]表现出最高的预测准确性和模型强度.

结论:

索博尔索引是分子分类的可靠拓描述符.
这种方法增强了对粉素物理化学行为的理解.
这些发现支持生物聚合物分析,材料科学和食品技术的应用.

关键词:

在这里,Python是Python.第二个索博尔指数索博尔指数是索博尔的指数.索博尔的参数指数指数.第三个索博尔指数拓学指数拓学指数

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