(RMSE: 0.257 to 0.710 and R2-value: -0.276 to 0.472). Hence, we decided to accept the values of parameters when using ECFP4 in scenario II, considering the reported superiority and generalizability of this fingerprint in related work ; hereafter, we describe the results for ECFP4. The predictive accuracy of kin (RMSE/R2-value: 0.257/0.427) and kout (RMSE/R2value: 0.325/0.472) was better than that of kdeep_br_in (RMSE/R2-value: 0.710/0.356) and kdeep_br_out (RMSE/R2-value: 0.664/-0.276). This lower accuracy with kdeep_br_in and kdeep_br_out could have been related to the range of the fitting setting (from 0.001 to 1), which was chosen to avoid the divergence of the estimated values. As a result, the predicted values of kdeep_br_in and kdeep_br_out tended to be located around the edge of the fitting range, which could make ML prediction difficult.