Abstract:
:Three-dimensional quantitative structure-activity relationship (3D-QSAR) modelling was conducted on a series of leucine-rich repeat kinase 2 (LRRK2) antagonists using CoMFA and CoMSIA methods. The data set, which consisted of 37 molecules, was divided into training and test subsets by using a hierarchical clustering method. Both CoMFA and CoMSIA models were derived using a training set on the basis of the common substructure-based alignment. The optimum PLS model built by CoMFA and CoMSIA provided satisfactory statistical results (q(2) = 0.589 and r(2) = 0.927 and q(2) = 0.473 and r(2) = 0.802, respectively). The external predictive ability of the models was evaluated by using seven compounds. Moreover, an external evaluation set with known experimental data was used to evaluate the external predictive ability of the porposed models. The statistical parameters indicated that CoMFA (after region focusing) has high predictive ability in comparison with standard CoMFA and CoMSIA models. Molecular docking was also performed on the most active compound to investigate the existence of interactions between the most active inhibitor and the LRRK2 receptor. Based on the obtained results and CoMFA contour maps, some features were introduced to provide useful insights for designing novel and potent LRRK2 inhibitors.
journal_name
SAR QSAR Environ Resjournal_title
SAR and QSAR in environmental researchauthors
Pourbasheer E,Aalizadeh Rdoi
10.1080/1062936X.2016.1184713subject
Has Abstractpub_date
2016-05-01 00:00:00pages
385-407issue
5eissn
1062-936Xissn
1029-046Xjournal_volume
27pub_type
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