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使用QSAR建模评估抗结直肠癌药物的量子化学预测剂的评估

Yasser Alharbi¹, Kusum Yadav¹, Lulwah M Alkwai¹

¹College of Computer Science and Engineering, University of Ha'il, Ha'il, Kingdom of Saudi Arabia.

Journal of computer-aided molecular design

|March 10, 2026

概括

机器学习模型使用分子和量子性质准确预测抗结肠直肠癌剂的活性. 额外的树木算法表现出色,为有效的药物发现提供了洞察力.

科学领域:

计算化学的计算化学
药理学药理学是指药理学的学科.
生物信息学是一种生物信息学.

背景情况:

优化抗结肠直肠癌剂预测对于药物开发至关重要.
机器学习 (ML) 为结构-生物活性关系分析提供了有效的方法.

研究的目的:

评估七种基于树的ML算法,用于预测抗结肠直肠癌剂活性.
通过使用量子化学性质来识别关键的分子预测因子并优化ML模型.

主要方法:

编制了一个关于分子描述符和量子化学性质的全面数据集.
应用梯度提升,随机森林,决策树,LightGBM,CatBoost,XGBoost和额外树的算法.
使用树结构的Parzen估计器进行超参数调整和k-fold交叉验证.

主要成果:

所有的ML模型都表现出对抗结肠直肠癌活性的高预测性能.
额外的树木算法在F1-Score和召回指标中表现出卓越的表现.
确定了关键的分子预测因素和相互作用术语,提高了模型的准确性.

结论:

基于ML的方法显著改善了对抗结肠直肠癌药物的活性预测.
额外的树木算法是药物发现中的定量结构-活性关系 (QSAR) 建模的一个有希望的工具.
这项研究为在抗结直肠癌药物设计中选择ML算法提供了宝贵的见解.

关键词:

抗结肠直肠癌药物抗结肠直肠癌药物药物发现药物发现机器学习 (ML) 是指机器学习.分子描述器分子描述器在QSAR建模中使用QSAR模型.量子化学属性量子化学属性

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