Exploring QSARs for inhibitory effect of a set of heterocyclic thrombin inhibitors by multilinear regression refined by artificial neural network and molecular docking simulations

Several non-peptide heterocyclic compounds reported as potent thrombin inhibitors in vitro were chosen to carry out a QSAR study upon them using MLR and ANN analysis. In order to identify the best QSAR models, the input for ANN consisted of those subsets of descriptors used in the MLR models. The best QSAR models contained the SIC0 descriptor as the main topological descriptor. To identify the physical and chemical properties involved in the ligandthrombin complexes, an automated ligand-flexible docking procedure was used. The docking results suggest that the thrombin inhibition by these heterocyclic compounds is driven by ππ, hydrogen bonds and salt bridge interactions. The best Gibbs free energy of ligand binding was found at the thrombin sites S1 and D. We have shown that it is possible to build MLR models with geometries taken from two different sources (semi-empirical and MD geometries) and obtain similar results.