使用基于图形的描述符和机器学习对细胞毒性化合物的定量研究
Shabbir Ahmad1, Sana Javed1, Sadia Khalid1
1Department of Mathematics, COMSATS University Islamabad, Lahore Campus, Pakistan.
Scientific reports
|January 12, 2026
概括
预测药物特性,如拓极地表面积 (Top_PSA),对于癌症药物开发至关重要. 这项研究表明,强大的预处理和LASSO回归有效地使用分子描述符预测Top_PSA.
科学领域:
- 计算化学的计算化学
- 药用化学 医学化学
- 药物发现 药物发现 药物发现
背景情况:
- 细胞毒药物对于癌症治疗至关重要,但具有狭窄的治疗指数和显著的副作用.
- 了解物理化学性质,例如拓极地表面积 (Top_PSA),对于预测药物吸收,分布和透性至关重要.
- Top_PSA是膜传输,被动扩散和血脑屏障透的关键指标.
研究的目的:
- 评估图形理论和分子描述符作为RDKit/Mordred计算的多种细胞毒剂的Top_PSA值的预测因素.
- 为了比较不同的数据预处理和特征选择策略,用于定量结构-活动关系 (QSAR) 建模.
- 为准确的Top_PSA预测确定最有效的建模方法.
主要方法:
- 计算了156种结构多样化的细胞毒剂的58个分子描述符.
- 应用了五种预处理方案:直接装配,PCA,强大的缩放,异常标识/消除以及基于VIF的特征选择.
- 使用线性,LASSO和回归模型与k-fold交叉验证.
- 分析了LASSO系数,以确定对Top_PSA的重要贡献因素.
主要成果:
- 强大的缩放与LASSO回归相结合,产生了最佳的预测性能,证明了在处理异种多样性和多对线性方面的有效性.
- 主要组件分析 (PCA) 提供了类似的预测准确性,但解释性降低.
- 基于VIF的修剪效果低于其他方法.
- 影响Top_PSA的重要因素包括异质原子含量,结合能力和负电子度加权指数.
结论:
- 强大的预处理和稀疏诱导规范化 (LASSO) 强烈推用于涉及众多描述者的QSAR研究.
- 清晰的计算管道对于可靠的Top_PSA预测至关重要.
- 该研究提供了QSAR工作流中的预处理,特征选择和模型选择的实际指导.
关键词:
细胞毒性药物 细胞毒性药物拉索回归是一种回归方式.机器学习是机器学习.分子描述器分子描述器异常值的调整.主要组成部分分析 (PCA)在回归模型中,回归模型是指回归模型.坡回归的回归方法拓索引 拓索引 拓索引更多相关视频
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