Based on previous studies, we aimed to build a more accurate model by filling the missing values with the predicted values. More specifically, we investigated the multitask message passing neural network (MPNN) model (Chemprop) and repeated RF model to predict the missing values and then built the RF model again using the predicted values as explanatory variables. Furthermore, to obtain a novel aspect of drug distribution through the analysis of the built models, we investigated the features of Kp values for each tissue as well as the relationship between tissues.