Several solutions are known to address the problem of missing values. In the area of drug discovery, accurate prediction of drug repositioning has been made possible by the incorporation of missingvalue predictions based on the similarity of compound structures . Missing data related to the activity values of different compound targets were predicted using the random forest (RF) method, and a QSAR model was constructed using the data from these predicted values as explanatory variables . In addition, to predict the drug clearance and volume of distribution in humans, the predicted values in other animals were used as explanatory variables .