Automatic feature subset selection for decision tree-based ensemble methods in the prediction of bioactivity

In the structure–activity relationship (SAR) study, a learning algorithm is usually faced with the problem of selecting a compact subset of descriptors related to the property of interest, while ignoring the rest. This paper presents a new method of molecular descriptor selection utilizing three commonly used decision tree (DT)-based ensemble methods coupled with a backward elimination strategy (BES). Our proposed method eliminates descriptor redundancy automatically and searches for more compact descriptor subset tailored to DT-based ensemble methods. Six real SAR datasets related to different categorical bioactivities of compounds are used to evaluate the proposed method. The results obtained in this study indicate that DT-based ensemble methods coupled with BES, especially boosting tree model, yield better classification performance for compounds related to ADMET.