SAnDReS 2.0:开发机器学习模型来探索得分函数空间
Walter Filgueira de Azevedo1, Rodrigo Quiroga2, Marcos Ariel Villarreal2
1Department of Physics, Institute of Exact Sciences, Federal University of Alfenas, Alfenas, Brazil.
我们开发了SAnDReS,这是一个结合AutoDock Vina和机器学习的新方法,用于预测蛋白质 - 连接体结合 afinity. SAnDReS模型的性能优于经典的评分功能,并且与其他先进的机器学习方法相匹配.
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.
- 机器学习 机器学习
背景情况:
- 经典的评分函数通常在预测蛋白质-连接体结合亲和力时缺乏准确性.
- 机器学习模型在结构和亲和数据上训练时,为特定的蛋白质系统提供了更好的预测性能.
研究的目的:
- 引入SAnDReS,这是开发机器学习模型来预测绑定亲和力的新方法.
- 通过将AutoDock Vina与各种回归方法集成来探索得分函数空间.
主要方法:
- 在 SAnDReS 中,AutoDock Vina 1.2 与 54 种 Scikit-Learn 回归方法相结合.
- 机器学习模型是使用晶体,对接和AlphaFold预测的蛋白质-连接体结构生成的.
- 通过三个案例研究来评估SAnDReS生成模型的性能.
主要成果:
- 在所有案例研究中,SAnDReS生成的模型在与经典评分函数相比显示出更高的性能.
- SAnDReS模型的预测准确性与现有的机器学习模型 (如KDEEP,CSM-lig和ΔVinaRF20) 相似或更好.
结论:
- SAnDReS提供了一种有效的方法,用于开发准确的结合亲和力预测模型.
- 该方法使得得得分函数空间的探索为增强的药物发现和分子建模.
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