索博尔拓指数和度的应用用于抗癌药物的QSPR建模:基于Python的方法
Yeliz Kara1, Yeşim Sağlam Özkan1, Ali Berkan Bektaş1
1Department of Mathematics, Faculty of Arts and Science, Bursa Uludag University, 16059, Bursa, Turkey.
索博尔拓指数对抗癌药物特性具有很强的预测能力,在化学信息学引导的药物发现中,在定量结构-属性关系建模中表现优于基于的措施.
科学领域:
- 药用化学 医学化学
- 化学信息学 化学信息学
- 计算化学计算化学
背景情况:
- 开发有效的抗癌药物是制药业的一个关键目标.
- 拓指数 (TI) 在化学信息学中对分子结构的表示非常有价值.
- 定量结构-属性关系 (QSPR) 建模利用IT进行预测分析.
研究的目的:
- 调查Sombor拓指数及其基于的变体在抗癌化合物的QSPR建模中的实用性.
- 评估这些指数对各种物理化学性质的预测性能.
主要方法:
- 利用图形理论和对抗癌症化合物的边缘分区方法.
- 开发了一个基于Python的框架来计算Sombor指数和度.
- 应用统计回归和机器学习技术用于QSPR模型开发.
主要成果:
- 与基于的指数相比,Sombor指数显示出更好的预测性能.
- 回归分析证实Sombor指数的统计学意义较高.
- QSPR模型准确地预测了诸如沸点,摩尔折射率和极化性等属性.
结论:
- 索博尔拓指数是用于QSPR建模的有希望的分子描述符.
- 这些指数为化学信息驱动的抗癌药物发现提供了强大的工具.
- 开发的计算框架促进了高效的QSPR分析.
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