The application of machine learning methods to the construction of quantitative structure–activity relationship models is a complex computational problem in which dimensionality reduction of the representation of the molecular structure plays a fundamental role in predicting a target activity. The feature selection pre-processing approach has been indicated to be effective in dimensionality reduction for building simpler and more understandable models. In this paper, a performance comparative study of 13 state-of-the-art feature selection filter methods is conducted. Structure–activity relationship models are constructed using three widely used classifiers and a diverse collection of datasets. The comparative study utilizes robust statistical tests to compare the algorithms. According to the experimental results, there are substantial differences in performance among the evaluated feature selection methods. The methods that exhibit the best performance are correlation-based feature selection, fast clustering-based feature selection and the set cover method.

机器学习方法在定量结构-活性关系模型的构建中的应用是一个复杂的计算问题，其中分子结构表示的降维在预测目标活性中起着基本作用。已经表明，特征选择预处理方法可以有效地减少维数，从而构建更简单，更易理解的模型。本文对13种最先进的特征选择滤波器方法进行了性能比较研究。结构-活动关系模型是使用三个广泛使用的分类器和各种数据集构建的。对比研究利用可靠的统计检验来比较算法。根据实验结果，在评估的特征选择方法之间，性能存在很大差异。表现最佳的方法是基于相关的特征选择，基于快速聚类的特征选择和集合覆盖方法。