Physicochemical atomic stereodescriptors (PAS) were implemented that represent the chirality of an atomic chiral center on the basis of empirical physicochemical properties of the ligands. The ligands are ranked according to a specific property, and the chiral center takes an S/R-like descriptor relative to that property. The procedure is performed for a series of properties, yielding a chirality profile. Application of the PAS descriptors to the prediction of enantioselectivity in chemical reactions, from the molecular structures, is illustrated here. The relationship between the molecular structures, represented by the PAS descriptors, and the enantioselectivity was learned by neural networks, decision trees, or random forests. In a first application, a data set was employed with chiral amino alcohols that enantioselectively catalyze the addition of diethylzinc to benzaldehyde. Prediction of the major enantiomer obtained in the reaction, from the molecular structure of the catalyst, was achieved with accuracy up to 90%. The second application investigated the enantiopreference of Pseudomonas cepacia lipase (PCL) toward primary alcohols. The learned models could make correct predictions about the preferred enantiomer, from the molecular structure of the substrate, in up to 93% of the cases. These included substrates with and without O-atoms bonded to the chiral center. The properties automatically selected to build the models can give indications on the relevant factors guiding the observed chemical behavior.