The variance threshold selection (VTS) approach is applied to remove the descriptors containing negligible variance σ 2 < 0.01, and the descriptor dimension is reduced from 17957 down to 4533. The 4533 descriptors, being denoted as VTS ensemble, include 938 topological descriptors and 3609 fingerprints. In order to reduce the dimension of VTS ensemble, the molecules of training sets were categorized into three distinct sample-subsets using mean shifted clustering method as shown by the color dots in the inset of Table . Each sample-subset was treated with the least absolute shrinkage and selection operator (Lasso) regression, being subject to the selection of target properties and the pre-determined penalty parameter, for the extraction of the targetproperty-dependent representative descriptors from VTS ensemble. For a particular target property, these extracted descriptors from three sample-subsets were emerged together (without double counting the duplicated ones) and formed the final X_LasY descriptor ensembles as summarized in Table , where X = H, L, or G denoting the target property -E H , E L , or ΔE HL and Y denotes the value of penalty parameter (0.1 or 0.5). The penalty parameter of 0.1 resulted in the dimension of descriptor space with more than 1200 descriptors for all target properties, and the other case -0.5 penalty could reduce VTS ensemble down to about 500 descriptors. 1 H, L, and G labels denote the target properties -E H , E L , and ΔE HL , respectively. The Las01 and Las05 labels denote the penalty parameter at 0.1 or 0.5 of Lasso regression. The larger penalty results in a smaller descriptor space. Duplicated descriptors found in the three sample-subsets were merged.