Back-propagation artificial neural networks (ANNs) were trained on a dataset of 104 VMAT2 ligands with experimentally measured log(1/K(i)) values. A set of related descriptors, including topological, geometrical, GETAWAY, aromaticity, and WHIM descriptors, was selected to build nonlinear quantitative structure-activity relationships. A partial least squares (PLS) regression model was also developed for comparison. The nonlinearity of the relationship between molecular descriptors and VMAT2 ligand activity was demonstrated. The obtained neural network model outperformed the PLS model in both the fitting and predictive ability. ANN analysis indicated that the computed activities were in excellent agreement with the experimentally observed values (r(2)=0.91, rmsd=0.225; predictive q(2)=0.82, loormsd=0.316). The generated models were further tested by use of an external prediction set of 15 molecules. The nonlinear ANN model has r(2)=0.93 and root-mean-square errors of 0.282 compared with the experimentally measured activity of the test set. The stability test of the model with regard to data division was found to be positive, indicating that the generated model is predictive. The modeling study also reflected the important role of atomic distribution in the molecules, size, and steric structure of the molecules when they interact with the target, VMAT2. The developed models are expected to be useful in the rational design of new chemical entities as ligands of VMAT2 and for directing synthesis of new molecules in the future.