强有力的询问基于机器学习的评分函数:它们在学习什么?
Guy Durant1, Fergus Boyles1, Kristian Birchall2
1Department of Statistics, University of Oxford, St Giles', Oxford OX1 3LB, United Kingdom.
Bioinformatics (Oxford, England)
|January 28, 2025
概括
机器学习评分函数经常学习数据集偏差,而不是物理. 我们的研究表明,简单的模型与复杂的模型相匹配,突出了偏差问题,并提供了测试性能的工具.
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:
- 基于机器学习的评分函数 (MLBSF) 在药物发现中至关重要,但通常表现不一致.
- 一个关键的局限性是他们倾向于学习数据集偏差而不是可概括的物理原理.
研究的目的:
- 严格评估受欢迎的MLBSF的性能.
- 调查MLBSF学习数据集偏差与物理属性的程度.
- 为MLBSFs提供一个强大的性能审讯平台.
主要方法:
- 对不同的MLBSF (RFScore,SIGN,OnionNet-2,Pafnucy,PointVS) 与拟议的基线模型进行比较.
- 对一系列基准进行评估,以评估预测准确性.
- 开发和使用ToolBoxSF平台进行绩效分析.
主要成果:
- 基线模型,仅用于学习数据集偏差,在大多数基准上实现了与流行的MLBSF相比的竞争性准确性.
- 这表明,许多当前的MLBSF主要捕获数据集特定的文物.
- 该研究提供了数据集偏差对MLBSF表现的重大影响的证据.
结论:
- 由于它们对数据集偏差的敏感性,目前的MLBSFs的概括性是可疑的.
- 研究人员需要批判性地评估MLBSF的表现和训练数据的影响.
- 工具箱SF平台为评估和改进MLBSF提供了宝贵的资源.
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