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

Abstract

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 ligand–thrombin complexes, an automated ligand-flexible docking procedure was used. The docking results suggest that the thrombin inhibition by these heterocyclic compounds is driven by π–π, 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.

Keywords::

• Artificial neuron network
• docking
• quantitative structure–activity relationship
• heterocyclic thrombin inhibitors

Acknowledgements

RGJ acknowledges partial support from CONACyT grant 46061-R. JCB acknowledges partial support from CONACyT grant 62488, ICyTDF and Fundación Miguel Aleman AC, EDI/SIP-SIBE/COFAA-IPN.

Declaration of interest

The authors report no conflicts of interest.

Notes

a MATLAB Version 5.3 1.29215a (R11.1), The MathWorks Inc., Natick, MA, 1999.

b SPSS Version 13 for Windows, Chicago, IL, 2004.

c MATLAB Version 7.2.0.232 (R2006a). The MathWorks Inc., Natick, MA, 2006.