Based on these data, the structural descriptor of the compound has been used as an explanatory variable to build data-driven machine learning models; however, most of them are 2-class classifiers , which might reflect the complex mechanism of absorption, as PK prediction of per os (p.o.) is more difficult than that of intravenous (i.v.) . Recently, two studies were conducted to predict Fa. In the first study in 2019, the authors aimed to use multiple-class classifiers using Caco-2 permeability and DMSO solubility as explanatory variables, and finally they built a 3-class classifier with high predictability with accuracy and kappa values of 0.836 and 0.560, respectively . In the second study, conducted in 2023, the authors focused on specific compounds with serotonergic activity and built a 2-class classifier (AUC 0.72 using the test set) and a regression model (R 2 = 0.047 using the test set) . Hence, to advance the prediction of human Fa, we aimed to build a quantitative regression structure-activity relationship (QSAR) model without dataset selection. The merit of a regression model is that it can show the exact number as a percentage of Fa, which can be easily understood by users. Disregarding dataset selection ensures the applicability domain of the predictive model.