To identify a more accurate model for brain concentration-time profiles, a practical situation was considered in which brain concentrations were only available for the 1-h time point. Based on the results mentioned earlier (3.3 ML application to the hybrid model (scenario II)), we used the predicted parameters of kin, kout, k_deep_br_in, kdeep_br_out from scenario II as the initial values and then started refitting parameters against compound brain concentrations at 1 h. The prediction accuracy data (scenario III) are shown in Table , Figure , and Figure , which demonstrate that the values of the reproduced simulated brain concentration-time profiles were much better (overall RMSE and R2-values: 0.246 and 0.805, respectively) than those of scenario II (overall RMSE and R2-values: 0.445 and 0.517, respectively). Moreover, comparing the results for scenario III' (overall RMSE and R2-values: 1.262 and 0.039, respectively), we observed a clear effect of the settings of initial values in the RF models. Hence, we conclude that this refitting approach can reproduce more accurate brain concentration-time profiles, and since the set situation is highly reflective of the practical situation in the drug discovery stage, we believe this method provides a new solution for use in research to identify CNS drugs.