Background: QSAR models as PLS, GFA, and 3D were developed for a series of matriptase
inhibitors using 35 piperidyl-cyclohexylurea compounds. The training and test sets were divided into a
set of 28 and 8 compounds, respectively and the pki values of each compound were used in the analysis.
Methods: Docking and alignment methodologies were used to develop models in 3D QSAR. The best
models among all were selected on the basis of regression statistics as r2, predictive r2 and Friedman
Lack of fit measure. Hydrogen donors and rotatable bonds were found to be positively correlated properties
for this target. The models were validated and used for the prediction of new compounds. Based
on the predictions of 3D-QSAR model, 17 new compounds were prepared and their activities were predicted
and compared with the active compound. Prediction of activities was performed for these 18
compounds using consensus results of all models. ADMET was also performed for the best-chosen
compound and compared with the known active.
Results and Conclusion: The developed model was able to validate the obtained results and can be
successfully used to predict new potential and active compounds.