Computational Prediction of Binding Affinity for CDK2-ligand Complexes.
A Protein Target for Cancer Drug Discovery

doi:10.2174/0929867328666210806105810
摘要

背景：CDK2 参与控制真核细胞周期进程。由于对用于药物开发的 CDK2 的极大兴趣和结晶这种酶的相对容易性，我们有超过 400 项结构研究集中在这种蛋白质靶点上。该结构数据是开发计算模型以估计 CDK2 配体结合亲和力的基础。目的：这项工作的重点是应用监督机器学习建模来开发评分函数以预测 CDK2 的结合亲和力的最新发展。方法：我们利用蛋白质数据库中可用的结构和从 BindingDB、Binding MOAD 和 PDBbind 访问的配体信息来评估机器学习技术的预测性能，并结合用于计算结合亲和力的物理建模。我们将这种混合方法与对接程序中可用的经典评分函数进行了比较。结果：我们对先前发布的模型的比较分析表明，使用质量弹簧系统和交叉验证的弹性网组合创建的模型预测 CDK2 抑制剂复合物的结合亲和力优于 AutoDock4 和 AutoDock Vina 中可用的经典评分函数。结论：这里回顾的所有研究都表明，有针对性的机器学习模型在计算结合亲和力方面优于经典评分函数。特别是对于 CDK2，我们看到物理建模与监督机器学习技术的结合显示出改进的预测性能，以计算蛋白质-配体结合亲和力。这些结果在评分函数空间概念的应用中找到了理论支持。
关键词: 化学空间、物理建模、CDK2、评分函数空间、药物设计、晶体结构、机器学习
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DOI https://dx.doi.org/10.2174/0929867328666210806105810	Print ISSN 0929-8673
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当代药物化学

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