使用 Molegro 虚拟 Docker 针对 CDK9 的目标.
1Department of Physics, Institute of Exact Sciences, Federal University of Alfenas, Alfenas, MG, Brazil.
Methods in molecular biology (Clifton, N.J.)
|October 11, 2025
概括
本研究介绍了一种机器学习工作流程,用于预测循环素依赖性激酶9 (CDK9) 抑制. 该方法整合了对接模拟和回归建模,以加强针对癌症的药物发现.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 机器学习在药物发现中的作用
背景情况:
- 预测蛋白质 - 配体结合的亲和力对于药物发现至关重要.
- 在对接程序中的经典评分函数在预测性能上有局限性.
- 循环素依赖性激酶9 (CDK9) 是抗癌药物的潜在治疗标.
研究的目的:
- 开发和介绍一个工作流来构建一个神经网络模型来预测CDK9.9的抑制.
- 整合计算工具,以加强对得分函数空间的探索.
- 为了促进对CDK9抑制的有效回归模型的识别.
主要方法:
- 使用Molegro虚拟端口 (MVD) 进行对接模拟,能源计算和描述器生成.
- 雇佣了Molegro数据建模器 (MDM) 来使用MVD衍生功能的回归模型.
- 集成的MVD和MDM与Jupyter笔记本电脑,以简化工作流程.
- 来自BindingDB的结合亲和力数据和来自蛋白质数据库的蛋白质结构.
主要成果:
- 演示了一种结合MVD,MDM和Jupyter笔记本的工作流程,用于CDK9抑制预测.
- 成功构建了一个神经网络回归模型,用于预测CDK9抑制.
- 促进了评分函数空间的综合探索,以确定最佳的回归模型.
结论:
- 本工作流提供了一种可靠的方法来预测蛋白质-连接体结合亲和力,特别是对于CDK9.
- 这种综合计算策略提高了针对CDK9.9的潜在抗癌药物候选者的有效性.
- 开发的模型和数据集在GitHub上公开提供,以便进一步研究和应用.
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