The evaluation of possible interactions between chemical compounds and antitarget proteins is an important task of the research and development process. Here, we describe the development and validation of QSAR models for the prediction of antitarget end-points, created on the basis of multilevel and quantitative neighborhoods of atom descriptors and self-consistent regression. Data on 4000 chemical compounds interacting with 18 antitarget proteins (13 receptors, 2 enzymes, and 3 transporters) were used to model 32 sets of end-points (IC(50), K(i), and K(act)). Each set was randomly divided into training and test sets in a ratio of 80% to 20%, respectively. The test sets were used for external validation of QSAR models created on the basis of the training sets. The coverage of prediction for all test sets exceeded 95%, and for half of the test sets, it was 100%. The accuracy of prediction for 29 of the end-points, based on the external test sets, was typically in the range of R(2)(test) = 0.6-0.9; three tests sets had lower R(2)(test) values, specifically 0.55-0.6. The proposed approach showed a reasonable accuracy of prediction for 91% of the antitarget end-points and high coverage for all external test sets. On the basis of the created models, we have developed a freely available online service for in silico prediction of 32 antitarget end-points: http://www.pharmaexpert.ru/GUSAR/antitargets.html.