Feature selection consistently affects the performance of model trained with KNN and four ECFPs (Table 6 and Tables S17–S19). Here KNN method with 1024bit-ECFP4 is taken as an example (Table 6). The models trained with 512 features (AM25, F1 = 0.911) or 256 features (AM49, F1 = 0.920) manifest the significantly different performance compared to its counterpart trained with full features (AM01, F1 = 0.898) according to the criterion (p-value < 0.0001). Based on the average F1-score, in this case feature number 512 or 256 after feature selection is better than the full features. However, it is not always true that feature selection will be helpful to the KNN with 1024bit-ECFP4. The models trained with 128 features (AM73, F1 = 0.910) and full features (AM01, F1 = 0.898) have the similar performance, since two-sample T-test illustrates that there are no significant difference of performance between these two models, since the p-value is larger than 0.0001. Thus, feature selection will improve the performance of KNN method depending on the specific feature number. KNN method with the other ECFPs (Tables S17–S19) gives the same conclusion.