More specifically, as described in the previous section about the model-training without the feature selection, 76 runs of random forest are conducted considering the combination of four ECFP fingerprints and different random splits of the dataset, which will lead to 76 models and the attendant 76 sets of feature importance. Then the feature importance for all the bits in the ECFP fingerprint is sorted descendingly and plotted in Figures S7–S10. Thus, the top 512, 256, and 128 important features (Figures S7–S10) are selected respectively as the typical feature subsets for the following model-training, since the exhaustive and systematic scan of feature number ranging from 1 to fingerprint length is really time-consuming especially for the training of deep neuron networks such as DNN2 and DNN3.