Fig. 3
From: Blood–brain barrier penetration prediction enhanced by uncertainty estimation
Prediction performance by introducing different uncertainty estimation methods for BBBp prediction models. a The MCC curves for different uncertainty estimation methods in GROVER, namely Entropy, MC-dropout, Multi-initial, FPsDist, LatentDist and random method. The x-axis is the proportion of remaining compounds in S-data when the compounds with high uncertainty are sequentially discarded, and y-axis is corresponding MCC of the BBBp prediction model. The MCC_AUC is shown in parentheses. b The MCC curves for different uncertainty estimation methods in Attentive FP. c The MCC curves for different uncertainty estimation methods in MLP(PCP). d The MCC curves for different uncertainty estimation methods in RF(PCP)