QSAR study of prolylcarboxypeptidase inhibitors by genetic algorithm: Multiple linear regressions

Journal of Chemical Sciences

Reza Aalizadeh⁴  Mohammad Reza Ganjali4¹  Alireza Banaei²  Saadat Vahdani³  Eslam Pourbasheer1² 

[1] Center of Excellence in Electrochemistry, Faculty of Chemistry, University of Tehran, P. O. Box 14155-6455, Tehran, Iran$$;Department of Chemistry, Payame Noor University (PNU), P. O. Box, 19395-3697 Tehran, Iran$$;Department of Chemistry, Islamic Azad University-North Tehran Branch, Tehran, Iran$$;Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece$$

关键词: QSAR;    hierarchical clustering;    genetic algorithms;    Prolylcarboxypeptidase (PrCP).;   

DOI  :  

来源: Indian Academy of Sciences

【 摘 要 】

The predictive analysis based on quantitative structure activity relationships (QSAR) on benzim-idazolepyrrolidinyl amides as prolylcarboxypeptidase (PrCP) inhibitors was performed. Molecules were represented by chemical descriptors that encode constitutional, topological, geometrical, and electronic structure features. The hierarchical clustering method was used to classify the dataset into training and test subsets. The important descriptors were selected with the aid of the genetic algorithm method. The QSAR model was constructed, using the multiple linear regressions (MLR), and its robustness and predictability were verified by internal and external cross-validation methods. Furthermore, the calculation of the domain of applicability defines the area of reliable predictions. The root mean square errors (RMSE) of the training set and the test set for GA-MLR model were calculated to be 0.176, 0.279 and the correlation coefficients (R2) were obtained to be 0.839, 0.923, respectively. The proposed model has good stability, robustness and predictability when verified by internal and external validation.

【 授权许可】

Unknown