The development of new therapies to treat hypertension effectively is currently an intensive area of research. To achieve this objective quantitative structure activity relationship (QSAR) study was carried out on a reported series of phthalazine derivatives as alpha-1d antagonist, as it provides the rationale for the changes in the pharmacophore to have more potent and less toxic analogue. In this article, we report 2D and 3D QSAR studies for the set of 20, alpha-1d antagonist. For the 2D QSAR studies we used stastical methods like multiple linear regression (MLR), principle component regression (PCR), and partial least square regression (PLSR) technique. The 3D QSAR model was developed by Simulated Annealing kohonen Nearest Neighbour Molecular Field Analysis (SA kNN MFA). By performing 2D QSAR, we found that multiple linear regression method showed best statistical result when compared with other methods. The model has shown correlation coefficient (r2), cross validation (q2) and external validation (pred_r2) values of 0.8639, 0.7839 and 0.8621, respectively. The 3D QSAR models were generated to study the effect of steric, electrostatic and hydrophobic descriptors on alpha-1d antagonist activity. The model with good external and internal productivity for the training and test set that has shown cross validation (q2) and external validation (pred_r2) values of 0.6053 and 0.8621, respectively. The steric and hydrophobic descriptors at the grid points S_517 and H_504 play an important role in the design of new molecule. Thus 2D and 3D QSAR studies were found to reliable clues for further optimization of phthalazine pharmacophore as alpha-1d antagonist.
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