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机器学习驱动的QSAR模拟抗癌活动从合理设计的合成黄库

Natthanan Vijara¹, Borwornlak Toopradab^2,3, Jantana Yahuafai⁴

¹Center of Excellence in Natural Products Chemistry, Department of Chemistry, Faculty of Science, Chulalongkorn University, Bangkok, 10330, Thailand.

|May 30, 2025

概括

这项研究开发了一种机器学习定量结构-活性关系 (QSAR) 模型,用于设计强效的抗癌类黄衍生物. 该模型确定了有前途的候选药物,它们对乳腺癌和肝癌细胞具有增强的细胞毒性.

科学领域:

药用化学医学化学
计算化学计算化学
药物发现药物发现药物发现

背景情况:

黄被认为是药物发现中的特权支架,显示出作为抗癌剂的重大前景.
优化用于抗癌药物开发的化合物需要有效的方法.
定量结构-活性关系 (QSAR) 模型可以加快强效药物候选者的识别.

研究的目的:

开发和验证一种基于机器学习的QSAR模型,用于抗癌类黄衍生物.
设计和合成具有针对癌症细胞系增强细胞毒性的新型黄类型.
确定影响黄抗癌活性的关键分子描述剂.

主要方法:

设计和合成89个使用药模拟器建模的黄类型.
对合成化合物的生物评估与乳腺癌 (MCF-7) 和肝癌 (HepG2) 细胞系,以及正常的Vero细胞.
开发和比较机器学习模型 (随机森林,极端梯度增强,人工神经网络) 用于QSAR分析.
使用测试化合物和SHapley添加式扩展 (SHAP) 进行描述器分析,验证表现最佳的模型.

主要成果:

有前途的黄候选物对MCF-7和HepG2癌细胞具有增强的细胞毒性,对正常细胞的毒性较低.
随机森林 (RF) 模型表现出卓越的性能,达到0.820 (MCF-7) 和0.835 (HepG2) 的R2值.
交叉验证 (R2cv) 和测试组验证证实了QSAR模型的稳定性,RMSEtest值为0.573 (MCF-7) 和0.563 (HepG2).
SHAP分析确定了影响抗癌活性的关键分子描述剂,有助于合理的药物设计.

结论:

一个强大的机器学习驱动的QSAR模型成功地开发了抗癌类黄衍生物.
该模型促进了选择性和强大的抗癌剂的合理设计.
这种方法加快了抗癌药物发现中化合物的优化.

关键词:

它具有抗癌活性,具有抗癌活性.发现药物的发现.这种类型的黄素是flavones.机器学习是机器学习.定量结构活动关系

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