使用增强数据缩小机器学习评分函数和自由能量扰动之间的差距
Ísak Valsson1, Matthew T Warren2, Charlotte M Deane1
1Oxford Protein Informatics Group, Department of Statistics, University of Oxford, Oxford, UK.
Communications chemistry
|February 8, 2025
概括
我们开发了一个新的机器学习模型,AEV-PLIG,用于准确的结合亲和力预测. 这种模型表现出具有竞争力的性能,并且比传统的基于物理的方法快得多,有助于药物发现.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习是机器学习.
背景情况:
- 机器学习模型承诺快速结合的亲和力预测,但缺乏对药物发现任务 (如优化) 的强有力的评估.
- 目前的模型在排名同源联体序列方面扎,这限制了它们在现实世界中的应用.
研究的目的:
- 引入一种基于注意力的新型图形神经网络,AEV-PLIG,以改进结合亲和力预测.
- 为评估机器学习模型开发一种新的,现实的分销外测试集 (OOD测试).
- 严格评估机器学习模型的性能与基于物理的方法.
主要方法:
- 开发了基于注意力的图形神经网络模型AEV-PLIG.
- 引入了用于分销之外评估的OOD测试套件.
- 在OOD测试上使用基准AEV-PLIG,CASF-2016和自由能量扰动 (FEP) 基准.
主要成果:
- 与基于物理的方法相比,AEV-PLIG显示出具有竞争力的性能.
- 增强数据显著改善了预测相关性和FEP基准的排名 (PCC:0.41到0.59,肯德尔的t:0.26到0.42).
- AEV-PLIG的计算速度大约是FEP计算的40万倍.
结论:
- AEV-PLIG提供了一种强大而高效的机器学习方法,用于绑定亲和力预测.
- 开发的策略与FEP计算弥合了绩效差距,提高了药物发现效率.
- AEV-PLIG为药物发现应用程序的机器学习模型提供了现实的评估.
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