Prediction of unknown properties for a target compound using quantitative structure-property relationships (QSPRs) is reliable only if the compound is within the model applicability domain. To improve the prediction reliability, a "targeted" QSPR (TQSPR) method is developed in which a training set containing only compounds structurally similar to the target compound is first identified. Similarity is measured by the partial correlation coefficients between the vectors of the molecular descriptors of the target compound and each potential predictive compound. Available property data in the training set are then used in the usual manner to select molecular descriptors for QSPRs, predicting the properties of the target and the rest of the compounds in the set. Preliminary results show that the method proposed yields predictions within the experimental error for compounds well represented in the database and fairly reliable estimates for complex compounds that are sparsely represented. The cutoff value of the partial correlation coefficient provides an indication of the expected prediction error.