用SAnDReS预测CDK2的抑制:机器学习的应用来导航得分函数空间
1Department of Physics, Institute of Exact Sciences, Federal University of Alfenas, Av. Jovino Fernandes de Sales 2600, Bairro Santa Clara, Alfenas, MG., 37133-840, Brazil.
Current medicinal chemistry
|September 2, 2024
概括
机器学习模型可以有效地预测循环素依赖激酶2 (CDK2) 的抑制. 与其他计算方法相比,SAnDReS程序为CDK2抑制剂构建了优越的预测模型.
科学领域:
- 计算化学是一种计算化学.
- 结构生物学是结构生物学.
- 药物发现 药物发现
背景情况:
- 循环素依赖性激酶2 (CDK2) 对于细胞循环进展至关重要,也是抗癌疗法的关键标.
- 30年来关于CDK2的广泛结构数据使得开发用于研究其抑制的计算模型成为可能.
- 机器学习 (ML) 模型越来越多地用于预测药物向相互作用.
研究的目的:
- 审查计算模型的应用,特别是ML,用于估计CDK2抑制.
- 评估使用SAnDReS程序构建的ML模型对CDK2抑制剂的预测性能.
- 将SAnDReS生成的ML模型与经典和深度学习评分函数进行比较.
主要方法:
- 在PubMed上进行文献搜索,寻找预测CDK2抑制的ML模型.
- 使用CDK2的BindingDB数据来生成更新的ML模型.
- 应用SAnDReS程序来建模CDK2抑制剂相互作用,并使用DOME分析分析预测性能.
主要成果:
- 使用SAnDReS构建的ML模型显示,与经典和深度学习评分函数相比,CDK2抑制 (pKi) 的预测性能优越.
- DOME分析证实了ML模型适用于预测蛋白质-配体相互作用的适用性.
- 该研究验证了SAnDReS用于开发CDK2抑制剂高性能预测模型的使用.
结论:
- 丰富的CDK2结构和功能数据支持开发强大的ML模型来预测其抑制.
- SAnDReS提供了一种强大的计算方法,用于构建先进的ML模型来预测pKi,优于现有的评分功能.
- 这项工作突出了ML和SAnDReS在促进发现针对CDK2的抗癌药物的潜力.
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