The aim of this study was to investigate the feasibility of a quantitative structure-pharmacokinetic relationships (QSPKR) method based on contemporary three-dimensional (3D) molecular characterization and multivariate statistical analysis. For this purpose, the programs SYBYL/CoMFA, GRID, and Pallas, in combination with the multivariate statistical technique principal component analysis were employed to generate a total of 16 descriptor variables for a series of 12 structurally related adenosine A1 receptor agonists. Subsequently, the multivariate regression method, partial least squares, was used to predict clearance (CL), volume of distribution (VdSS) and protein binding (fraction unbound, fU). The QSPKR models obtained could account for most of the variation in CL, VdSS, and fU (R2 = 0.82, 0.61 and 0.78, respectively). Cross-validation confirmed the predictive ability of the models (Q2 = 0.59, 0.41 and 0.62 for CL, VdSS, and fU, respectively). In conclusion, we have developed a multivariate 3D QSPKR model that could adequately predict overall pharmacokinetic behavior of adenosine A1 receptor agonists in rat. This methodology can also be used for other classes of compounds and may facilitate the further integration of QSPKR in drug discovery and preclinical development.