Computational neural network analysis of the affinity of lobeline and tetrabenazine analogs for the vesicular monoamine transporter-2

Back-propagation artificial neural networks (ANNs) were trained on a dataset of 104 VMAT2 ligands with experimentally measured log(1/Ki) 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 (r2 = 0.91, rmsd = 0.225; predictive q2 = 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 r2 = 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.

Partial least square regression and neural network approaches were used to build linear and nonlinear QSAR models based on a set of 104 tetrabenazine and lobeline analogs that are ligands for the vesicular monoamine transporter-2 (VMAT2). It was demonstrated that a fully interconnected three-layer neural network model trained with the back-propagation procedure could learn the correct relationship between a set of relevant molecular descriptors of the compounds and their log(1/Ki) values for VMAT2 better than the partial least squares approach.Figure optionsDownload as PowerPoint slide