2D and 3D-QSAR analysis of pyrazole-thiazolinone derivatives as EGFR kinase inhibitors by CoMFA and CoMSIA

Title:2D and 3D-QSAR analysis of pyrazole-thiazolinone derivatives as EGFR kinase inhibitors by CoMFA and CoMSIA

Volume: 11 Issue: 4

Author(s): Eslam Pourbasheer, Reza Aalizadeh, Hamid Mohammad Shiri, Alireza Banaei and Mohammad Reza Ganjali

Affiliation:

Keywords: 3D-QSAR, 2D-QSAR, pyrazolyl-thiazolinone, CoMFA, CoMSIA.

Abstract: Two and Three-dimensional quantitative structure-activity relationship (2D, 3D-QSAR) study was performed for some pyrazole-thiazolinone derivatives as EGFR kinase inhibitors using the CoMFA, CoMSIA and GA-MLR methods. The utilized data set was split into training and test set based on hierarchical clustering technique. From the five CoMSIA descriptors, electrostatic field presented the highest correlation with the activity. The statistical parameters for the CoMFA (r²=0.862, q²=0.644) and CoMSIA (r²=0.851, q²=0.740) were obtained for the training set with the common substructure-based alignment. The obtained parameters indicated the superiority of the CoMSIA model over the CoMFA model. A test set consisted of seven compounds was used to evaluate the proposed models. The results of contour maps which were presented by each method lead to some insights for increasing the inhibition activity of compounds. The 2D-QSAR model was built based on three descriptors selected by genetic algorithm and showed high predictive ability (R² _train= 0.843, Q² _LOO=0.787). Molecular docking study was also performed to understand the type interactions presented in binding site of the receptor and ligand. The developed models in parallel with molecular docking can be employed to design and derive novel compounds with the potent EGFR inhibitory activity.

Cite this article as:

Pourbasheer Eslam, Aalizadeh Reza, Shiri Mohammad Hamid, Banaei Alireza and Ganjali Reza Mohammad, 2D and 3D-QSAR analysis of pyrazole-thiazolinone derivatives as EGFR kinase inhibitors by CoMFA and CoMSIA, Current Computer-Aided Drug Design 2015; 11 (4) . https://dx.doi.org/10.2174/1573409912666151106120058