QSAR Study of 17β-HSD3 Inhibitors by Genetic Algorithm-Support Vector Machine as a Target Receptor for the Treatment of Prostate Cancer

Document Type: Research article

Authors

1 Department of Chemistry, Payame Noor University Tehran, Iran.

2 Department of Chemistry, Islamic Azad University-North Tehran Branch, Tehran, Iran.

3 Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece.

4 Department of Chemistry, Kazerun Branch, Islamic Azad University, Kazerun, Iran.

Abstract

The 17β-HSD3 enzyme plays a key role in treatment of prostate cancer and small inhibitors
can be used to efficiently target it. In the present study, the multiple linear regression (MLR),
and support vector machine (SVM) methods were used to interpret the chemical structural
functionality against the inhibition activity of some 17β-HSD3inhibitors. Chemical structural
information were described through various types of molecular descriptors and genetic algorithm
(GA) was applied to decrease the complexity of inhibition pathway to a few relevant molecular
descriptors. Non-linear method (GA-SVM) showed to be better than the linear (GA-MLR)
method in terms of the internal and the external prediction accuracy. The SVM model, with
high statistical significance (R2
train = 0.938; R2
test = 0.870), was found to be useful for estimating
the inhibition activity of 17β-HSD3 inhibitors. The models were validated rigorously through
leave-one-out cross-validation and several compounds as external test set. Furthermore, the
external predictive power of the proposed model was examined by considering modified R2 and
concordance correlation coefficient values, Golbraikh and Tropsha acceptable model criteriaʹs,
and an extra evaluation set from an external data set. Applicability domain of the linear model
was carefully defined using Williams plot. Moreover, Euclidean based applicability domain
was applied to define the chemical structural diversity of the evaluation set and training set.

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