Quantitative structure-activity relationship (QSAR) studies on a series of 2-phenylindole derivatives as anticancer drugs were performed to choice the important descriptor, which is responsible for their anticancer activity (expressed as pIC 50 ). The geometry optimizations were performed on the structures using Gaussian software with the density functional B3LYP and 6-311G(d,p) basis sets.Dragon software was used to calculate molecular descriptors, and the genetic algorithm (GA) procedure and backward regression were used to proper selection of the most relevant descriptors. The backward multiple linear regression (BW-MLR) and backpropagation-artificial artificial neural network (BP-ANN) were carried out to design QSAR models. The squared correlation coefficient (R 2 ) and the root mean squared error (RMSE) values of the GA-MLR model were calculated to be 0.2843 and 0.7001, respectively. The BP-ANN model was the most powerful, with the square of predictive correlation coefficient R 2 pred , root mean square error (RMSE), and absolute average deviation (AAD) which was equal to 0.9416, 0.0238, and 0.0099, respectively.
Quantitative structure-activity relationship (QSAR) studies on a series
of 2-phenylindole derivatives as anticancer drugs were performed to
choice the important molecular descriptor which is responsible for their
anticancer activity (expressed as pIC50)). The geometry optimizations
were performed on the structures using Gaussian 09W software with the
density functional B3LYP and 6-311G(d,p) basis sets . Dragon 5.4
software was used to calculate molecular descriptors, and the genetic
algorithm (GA) procedure and backward regression were used to proper
selection of the most relevant descriptors. Different chemometric tools
including the backward multiple linear regression (BW- MLR) and
backpropagation-artificial artificial neural network (BP-ANN) were
carried out to design QSAR models. The squared correlation coefficient
(R2) and the Root Mean Squared Error (RMSE) values of the GA-MLR model
were calculated to be 0.2843 and 0.7001 respectively. The BP-ANN model
was the most powerful, with the square of predictive correlation
coefficient R2pred, root mean square error (RMSE), and absolute average
deviation (AAD) which was equal to 0.9416, 0.0238, and 0.0099,
respectively. The external validation criteria (Q2F1, Q2F2, Q2F3, and
concordance correlation coefficient were applied to assay predictive
efficiency of QSAR model derived by BP-ANN method. The results derived
from the BP-ANN indicated that the anticancer activity of 2-phenylindole
derivatives depends strongly on 3D descriptors namely Radial
Distribution Function (RDF) descriptors and 3D-molecular geometry of the
studied compounds play an important role for these activities. Thus, it
could be useful in the design of new 2-phenylindole derivatives having
anticancer potency.
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