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Review Article

机器学习技术在预测药物靶点结合亲和力中的应用:周期蛋白依赖性激酶2的研究

卷 28, 期 2, 2021

发表于: 02 November, 2019

页: [253 - 265] 页: 13

弟呕挨: 10.2174/2213275912666191102162959

价格: $65

摘要

背景：对周期蛋白依赖性激酶2 (CDK2)结构的阐明，使开发虚拟筛选的靶向评分功能成为可能，旨在识别该酶的新抑制剂。CDK2是一种用于开发调节细胞周期进展和控制药物的蛋白靶点。这类药物具有潜在的抗癌活性。对周期蛋白依赖性激酶2 (CDK2)结构的阐明，使开发虚拟筛选的靶向评分功能成为可能，旨在识别该酶的新抑制剂。CDK2是一种用于开发调节细胞周期进展和控制药物的蛋白靶点。这类药物具有潜在的抗癌活性。 目的：我们的目标是回顾机器学习方法在预测蛋白质靶点配体结合亲和力方面的最新应用。为了评估经典评分函数和目标评分函数的预测性能，我们集中分析了CDK2结构。 方法：我们有数百个CDK2与不同配体的二元配合物的实验结构数据，其中许多具有抑制常数信息。我们通过经典的评分函数和机器学习模型来研究计算CDK2结合亲和力的计算方法。 结果：对Molegro Virtual Docker、AutoDock4、Autodock Vina等对接程序中可用的经典评分函数的预测性能分析表明，这些方法未能预测出与实验数据具有显著相关性的绑定亲和力。通过监督机器学习技术开发的目标评分函数与实验数据显示显著相关。 结论：在这里，我们描述了有监督机器学习技术的应用，以生成一个评分函数来预测绑定亲和力。与经典评分函数相比，机器学习模型显示出了优越的预测性能。分析通过机器学习获得的计算模型可以捕捉到与CDK2结合亲和力的基本结构特征。

关键词: 机器学习，质点系统，CDK2，激酶，癌症，药物设计。

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