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Review Article

SAnDReS基于机器学习的评分功能，开发和应用

卷 28, 期 9, 2021

发表于: 15 May, 2020

页: [1746 - 1756] 页: 11

弟呕挨: 10.2174/0929867327666200515101820

价格: $65

摘要

背景：蛋白质-配体配合物的原子坐标分析可以提供三维数据来生成计算模型，以评估结合亲和力和热力学状态函数。机器学习技术的应用可以创建模型来评估蛋白质-配体的势能和结合亲和力。与对接程序中可用的经典评分功能相比，这些方法显示出出众的预测性能。 目标：我们的目的是审查SAnDReS程序的开发和应用。我们描述了机器学习模型的创建，以评估蛋白质-配体复合物的结合亲和力。 方法：SAnDReS实现scikit-learn库中可用的机器学习方法。该程序可从https://github.com/azevedolab/sandres下载。 SAnDReS使用晶体结构，结合和热力学数据来创建目标评分功能。 结果：程序SAnDReS在诸如凝血因子Xa，细胞周期蛋白依赖性激酶和HIV-1蛋白酶等药物靶标上的最新应用能够创建靶向评分功能，以预测这些蛋白的抑制作用。这些目标模型的表现优于经典评分功能。 结论：在这里，我们回顾了通过应用SAnDReS程序预测绑定亲和力的机器学习评分功能的发展。我们的研究表明，与AutoDock4，Molegro Virtual Docker和AutoDock Vina等程序中提供的经典评分功能相比，SAnDReS开发的模型具有出色的预测性能。

关键词: 机器学习，SAnDReS，细胞周期蛋白依赖性激酶，蛋白质-配体相互作用，结合亲和力，吉布斯自由能。

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