从图形理论到化学信息学:修改的基于债券的指数和基于假设的多任务QSAR/QSPR基准
Azzam Altairi1, Zaied Alhaj2,3, Mohammed Alsharafi4,5,6
1Department of Biomedical Engineering, Institute of Graduate Studies, Istanbul University - Cerrahpasa, Istanbul, Turkey.
Scientific reports
|February 21, 2026
概括
我们介绍了新的修改的基于键的图形描述器,这些描述器结合了顶点和键信息,以改进化学信息学建模. 这些新描述符在定量结构-活动关系 (QSAR) 研究中显示出增强的预测能力.
科学领域:
- 化学信息学和化学信息学
- 数学化学 数学化学
- 图形理论 图形理论
背景情况:
- 经典的基于图形理论的基于度的描述符在化学信息学和QSPR/QSAR中至关重要,但通常只关注顶点度或乘数键贡献.
- 现有的方法缺乏统一的方法来有效地整合顶点和债券信息,从而限制了它们的描述和预测能力.
研究的目的:
- 引入和系统地研究一种新的基准图形描述器家族.
- 为这些描述符开发一个灵活和分析可处理的框架,适用于化学信息学中的机器学习应用.
主要方法:
- 引入了基于债券的修改后的指数,以局部债券因子和顶点内核加权边缘.
- 开发了一种对称内核的统一边缘分区表示,将索引表达为对等级的总和.
- 对于各种基准图表家族 (路径,循环,完整图表等) 上的16个修改指数的衍生闭式表达式. ) 的情况.
- 获得了指数子集的尖度极限,在正则图中表征平等.
主要成果:
- 介绍了一个由十六个修改的基于债券的图形描述器组成的新型家族.
- 在各种图形结构中为这些描述符衍生出分析表达式,揭示了非对称的增长模式和极端属性.
- 在抗菌分子上的大型多任务QSAR/QSPR管道中证明了这些描述者的优越预测效用,优于物理化学描述器.
结论:
- 拟议的修改后的基于债券的描述符提供了一种灵活和分析可处理的方式来合顶点和债券信息.
- 这些描述符非常适合作为现代化学信息学和基于图形的机器学习模型的结构性特征.
- 在QSAR/QSPR研究中增强的预测性能验证了它们在建模分子性质方面的实用性.
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