In a search of newer and potent antileishmanial (against promastigotes and amastigotes form of parasites) drug, a series of 60 variously substituted acridines derivatives were subjected to a quantitative structure activity relationship (QSAR) analysis for studying, interpreting, and predicting activities and designing new compounds by using multiple linear regression and artificial neural network (ANN) methods. The used descriptors were computed with Gaussian 03, ACD/ChemSketch, Marvin Sketch, and ChemOffice programs. The QSAR models developed were validated according to the principles set up by the Organisation for Economic Co-operation and Development (OECD). The principal component analysis (PCA) has been used to select descriptors that show a high correlation with activities. The univariate partitioning (UP) method was used to divide the dataset into training and test sets. The multiple linear regression (MLR) method showed a correlation coefficient of 0.850 and 0.814 for antileishmanial activities against promastigotes and amastigotes forms of parasites, respectively. Internal and external validations were used to determine the statistical quality of QSAR of the two MLR models. The artificial neural network (ANN) method, considering the relevant descriptors obtained from the MLR, showed a correlation coefficient of 0.933 and 0.918 with 7-3-1 and 6-3-1 ANN models architecture for antileishmanial activities against promastigotes and amastigotes forms of parasites, respectively. The applicability domain of MLR models was investigated using simple and leverage approaches to detect outliers and outsides compounds. The effects of different descriptors in the activities were described and used to study and design new compounds with higher activities compared to the existing ones.

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Please cite this article as: S. Chtita, R. Hmamouchi, M. Larif, M. Ghamali, M. Bouachrine, T. Lakhlifi, QSPR studies of 9-aniliioacridine derivatives for their DNA drug binding properties based on density functional theory using statistical methods: Model, validation and influencing factors, Journal of Taibah University for Science (2015), http://dx.Abstract: As a continuation of our research on the development and optimization of the biological activities/proprieties of acridine derivatives, a series of 31 molecules based on 9-aniliioacridines (25 training set and 6 test set) were subjected to 3D quantitative structure propriety relationship QSPR analyses for their drug-DNA binding proprieties using multiple linear regression (MLR) and multiple non-linear regression (MNLR). Quantum chemical calculations using density functional theory (B3LYP/6-31G (d) DFT) methods was performed on the studied compounds and used to calculate the electronic and quantum chemical parameters. The models were used to predict the association constant of the DNA drug binding of the test set compounds, and the agreement between the experimental and predicted values was verified. The descriptors determined by QSPR studies were used for the study and design of new compounds. The statistical results indicate that the predicted values were in good agreement with the experimental results (r= 0.935 and r= 0.936 for MLR and MNLR, respectively). To validate the predictive power of the resulting models, the external validation multiple correlation coefficients were 0.932 and 0.939 for the MLR and the MNLR, respectively. These results show that both models possess a favourable estimation stability and good prediction power.