DFT-QSAR models to predict biological activities of carbamates, s-alkylcarbamathioates, ureas and chloroacetamides derivatives

Azeddine Adad, Majdouline Larif, Rachid Hmammouchi, Abdelhafid Idrissi Taghki, Mohammed Bouachrine, Tahar Lakhlifi

Abstract


3D-QSAR study is applied to a set of 76 molecules based on 4 sets of compounds. This study was conducted using the principal component analysis (PCA) method, the past least square regression method (PLS) and the artificial neural network (ANN). The predicted values of activities are in good agreement with the experimental results. The past least square regression method (PLS) techniques, considering the relevant, showed a correlation coefficient (R2) values more than 0,70 which is a good result. As a result of quantitative structure-activity relationships, we found that the model proposed in this study is constituted of major descriptors used to describe these molecules. The obtained results suggested that the proposed combination of several calculated parameters could be useful to predict the biological activity of carbamates, s-alkylcarbamathioates, ureas, chloroacetamides derivatives.

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