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长度可变的序列的结构特征对于的定量结构-活性关系

Y Zhang¹, K Li¹, Y Gan¹

¹Center for Informational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu, China.

SAR and QSAR in environmental research

|September 10, 2025

概括

一种名为残留描述器距离向量 (RDDV) 的新方法解决了长度可变序列 (LVPS) 的质定量结构-活性关系 (pQSAR) 建模方面的挑战. RDDV提供了一个一致的描述器矩阵,改进了pQSAR分析.

科学领域:

计算化学是一种计算化学.
生物信息学是一种生物信息学.
类学类学类学

背景情况:

的定量结构-活性关系 (pQSAR) 模型预测生物活性的功能.
氨基酸描述器 (AADs) 描述,但为长度变化的序列 (LVPSs) 创建不一致的数据.
现有的方法,如自动交叉共变率 (ACC),对于pQSAR中的LVPSs是有限的.

研究的目的:

引入和验证一种新的多变量方法,残留描述器-距离向量 (RDDV),用于pQSAR分析.
克服pQSAR中对LVPSs的长度依赖描述符的局限性.
在pQSAR建模中为LVPS提供一致的描述符表示.

主要方法:

开发了基于残留间伪相互作用潜力的残留描述器-距离向量 (RDDV) 方法.
使用固定数量的描述参数来表征序列,无论长度如何.
使用内部pQSAR数据集与各种AAD和回归工具验证了RDDV.
将RDDV性能与传统的自动交叉共变率 (ACC) 方法进行比较.

主要成果:

RDDV成功地为LVPSs生成了不变数量的描述参数.
在测试数据集中,RDDV方法在pQSAR分析中显示出有效性.
对比分析显示了RDDV在特定pQSAR应用中作为ACC的替代品的潜力.
系统性探索揭示了AAD和RDDV的回归工具的最佳组合.

结论:

RDDV是一种有前途的第二代多变量方法,用于基于AD的pQSAR.
该方法有效地解决了LVPS描述器不一致的挑战.
RDDV为计算类学和药物发现提供了一种有价值的新方法.

关键词:

的定量结构-活性关系关系.氨基酸描述符的氨基酸描述符计算类学计算类学长度变化的序列.酸序列的表征表征.其余物描述器-距离向量

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