In this study, a quantitative structure-pharmacokinetic relationship (QSPkR) model for the volume of distribution (Vd) values of 126 anti-infective drugs in humans was developed employing multiple linear regression (MLR), artificial neural network (ANN) and support vector regression (SVM) using theoretical molecular structural descriptors. A correlation-based feature selection (CFS) was employed to select the relevant descriptors for modeling. The model results show that the main factors governing Vd of anti-infective drugs are 3D molecular representations of atomic van der Waals volumes and Sanderson electronegativities, number of aliphatic and aromatic amino groups, number of beta-lactam rings and topological 2D shape of the molecule. Model predictivity was evaluated by external validation, using a variety of statistical tests and the SVM model demonstrated better performance compared to other models. The developed models can be used to predict the Vd values of anti-infective drugs. U radu je odreðen kvantitativni odnos strukture i farmakokineti~kih parametara (QSPkR) za volumen distribucije (Vd) 126 antiinfektivnih lijekova u ljudi koriste}i vi{estruku linearnu regresiju (MLR), umjetne neuronske mre`e (ANN), regresiju potpornim vektorima (SVM) i teorijske molekulske deskriptore. Selekcija na temelju korelacije (CFS) upotrjebljena je za izbor relevantnih deskriptora za modeliranje. Rezultati su pokazali da su glavni faktori koji utje~u na Vd antiinfektivnih lijekova 3D molekulski prikaz van der Waalsovih volumena atoma i Sandersonove elektronegativnosti, broj alifatskih i aromatskih skupina, broj beta-laktamskih prstena i topolo{ki 2D oblik molekule. Prediktivnost modela procijenjena je vanjskom validacijom, koriste}i razli~ite statisti~ke testove. SVM model pokazao se boljim od ostalih modela. Razvijeni model mo`e se upotrijebiti za predvi|anje vrijednosti Vd antiinfektivnih lijekova.