使用Molegro虚拟码头进行实践对接.
Damla Dere1, Sema Nur Pehlivan2, Amauri Duarte da Silva3
1Department of Molecular Biology and Genetics, Faculty of Engineering and Natural Sciences, Kadir Has University, Istanbul, Turkey.
Methods in molecular biology (Clifton, N.J.)
|October 11, 2025
概括
这项研究引入了一种新的工作流程,用于使用机器学习模型预测循环素依赖激酶2 (CDK2) 抑制. 将虚拟选与计算方法相结合,可以提高比传统评分函数的预测准确度.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 在生物信息学中的机器学习.
背景情况:
- 蛋白质 - 配体对接对于药物发现至关重要.
- 预测结合亲和力和抑制是计算上具有挑战性的.
- 循环素依赖性激酶2 (CDK2) 是癌症研究的一个关键目标.
研究的目的:
- 开发一个工作流程,用于构建回归模型来预测CDK2抑制.
- 将虚拟对接模拟与机器学习集成在一起,以提高预测.
- 创建有针对性的评分功能,以提高准确性.
主要方法:
- 使用Molegro虚拟对接器 (MVD) 进行蛋白质 - 连接体对接模拟.
- 在搜索中使用差异进化和在绑定亲和中使用MolDock/Plants分数.
- 使用 Scikit-Learn 构建机器学习模型,并使用对接衍生的原子坐标.
主要成果:
- 开发了准确预测CDK2抑制的回归模型.
- 与经典评分函数相比,实现了优越的预测性能.
- 展示了一种实用的方法来整合对接和机器学习.
结论:
- 拟议的工作流有效预测CDK2抑制.
- 在对接数据上训练的机器学习模型为标准评分函数提供了强大的替代方案.
- 该方法为加速针对CDK2.2的药物发现工作提供了有价值的工具.
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