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The unbound brain-to-plasma concentration ratio (K p,uu,brain ) is a parameter that indicates the extent of central nervous system penetration. Pharmaceutical companies build prediction models because many experiments are required to obtain K p,uu,brain . However, the lack of data hinders the design of an accurate prediction model. To construct a quantitative structure–activity relationship (QSAR) model with a small dataset of K p,uu,brain , we investigated whether the prediction accuracy could be improved by incorporating software-predicted brain penetration-related parameters (BPrPs) as explanatory variables for pharmacokinetic parameter prediction. We collected 88 compounds with experimental K p,uu,brain  from various official publications. Random forest was used as the machine learning model. First, we developed prediction models using only structural descriptors. Second, we verified the predictive accuracy of each model with the predicted values of BPrPs incorporated in various combinations. Third, the K p,uu,brain  of the in-house compounds was predicted and compared with the experimental values. The prediction accuracy was improved using five-fold cross-validation (RMSE = 0.455,  r2  = 0.726) by incorporating BPrPs. Additionally, this model was verified using an external in-house dataset. The result suggested that using BPrPs as explanatory variables improve the prediction accuracy of the K p,uu,brain  QSAR model when the available number of datasets is small.

QSAR model to predict K p,uu,brain with a small dataset, incorporating predicted values of related parameter