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The multidrug resistance (MDR) of cancer cells has become a great barrier to the success of chemotherapy. In this study, quantitative structure activity relationship (QSAR) modeling was applied to 46 1,4-dihydropyridine structures (DHPs), and some selected compounds were docked. QSAR was used to generate models and predict the MDR inhibitory activity for a series of 1,4-dihydropyridines (DHP). The DHPs were built and optimized using the Sybyl program (x1.2 version). Descriptor generation was done by DRAGON package. Docking was carried out using Auto Dock 4.2 software. Multiple linear regression, and partial least square were performed as QSAR modelgeneration methods. External validation, cross-validation (leave one out) and y-randomization were used as validation methods. The constructed model using stepwise-MLR and GA-PLS revealed good statistical parameters. In the final step all compounds were divided into two parts: symmetric (PLS) and asymmetric (MLR) 1,4-dihydropyridines and two other models were built. The square correlation coefficient (R2) and root mean square error (RMSE) for train set for GA-PLS were (R2 = 0.734, RMSE train = 0.26). The predictive ability of the models was found to be satisfactory and could be employed for designing new 1,4-dihydropyridines as potent MDR inhibitors in cancer treatment. 1,4- Dihydropyridine ring containing protonable nitrogen as scaffold could be proposed. Sulfur, ester, amide, acyle, ether, fragments are connected to a 1,4-dihydropyridine ring. Phenyl groups (with an electronegative substituent) as a lipophilic part are essential for the inhibitory effect. Copyright© Bentham Science Publishers; For any queries, please email at


Shirin Mollazadeh, Jamal Shamsara, Maryam Iman, Farzin Hadizadeh. Docking and QSAR Studies of 1,4-Dihydropyridine Derivatives as Anti- Cancer Agent. Recent patents on anti-cancer drug discovery. 2017;12(2):174-185

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PMID: 28137213

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