In order to statistically compare the overall performance of those four ECFP fingerprints in the context of each machine-learning method, two-sample T-test is conducted based on F1-scores from two sets of nineteen random data-splitting schemes, and is systematically performed for each pair (Table 5 and Tables S14–S16) except the model-training with DNN, since only three random data-splitting are done for DNN due to its demanding computational time and are not enough to perform T-test for the limited sample size. Thus in this section, only KNN, SVM, GBM, and RF are statistically compared with the p-value from two-sample T-test. For KNN, Table 5 shows that the average F1-scores for 1024bit-ECFP4 (AM01), 2048bit-ECFP4 (AM07), 1024bit-ECFP6 (AM13), and 2048bit-ECFP6 (AM19) are 0.898, 0.900, 0.898, and 0.898 respectively, and there are no significant differences among two ECFPs according to the criterion (p-value < 0.0001). Thus, different ECFPs won't statistically influence the F1-score for KNN with full features. Based on the Tables S14–S16, this conclusion is also hold for SVM, GBM, and RF from the statistical perspective.