First, to investigate the applicability of our concept in modeling, we compared the ML models built in this study by calculating the RMSE and R 2 -values. The results are shown in Table . When comparing CRF and MTC, the RMSE and R 2 -values of CRF/MTC in most tissues were 0.57/0.58 and 0.38/0.31 in brain, 0.42/0.51 and 0.69/0.52 in gut, 0.37/0.40 and 0.61/0.48 in heart, 0.40/0.42 and 0.64/0.59 in kidney, 0.46/0.53 and 0.71/0.61 in lung, 0.34/0.42 and 0.67/0.48 in muscle, and 0.33/0.37 and 0.37/0.19 in skin, respectively. This superiority of CRF to MTC indicates that, (1) the properties LogP, fu, Drug Class, and fi worked well, and this inference is consistent with several existing studies , and (2) multitasking in Chemprop did not work well as it automatically handled missing entries in the dataset by masking out the respective values in the loss function and these results indicate that this method of handling the missing values does not work well in these tissues. However, R 2 -value of MTC in adipose (0.39) was better than CRF (0.32), and in the liver both RMSE and R 2 -value of MTC (0.48 and 0.47) were better than CRF (0.52 and 0.38). This is in agreement with the specific tissue features, because adipose tissue has different components than the others and liver has different types of transporters ; hence, the properties used as explanatory variables might not be effective in these tissues.