Following previous studies, we employed standard deviations of predictions from GGAP-CPI-IntEns as the model uncertainties and explored the ability of enriching highly accurate predictions and potential relationships between uncertainty and AC. Since AC is defined as discontinuity between ligand structure and bioactivity that hard to be captured by QSAR modeling, it is likely that deep ensemble model may give higher uncertainties on AC data. Separated by activity type, the overall uncertainty distribution results shown in Fig. (C) prove this hypothesis, showcasing difference distributions between AC samples and non-AC samples on pK i , pEC 50 , and pIC 50 data. This phenomenon reveals that GGAP-CPI-IntEns can expose a proportion of AC data with higher uncertainty being assigned. Details including results calculated by other metrics and overall uncertainty distribution can be found in Fig. and Fig. .