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  • Chem Sci Trans., 2019, 8(1),  pp 77-90  

    DOI:10.7598/cst2019.1510

    Research Article

    QSAR Study of (5-Nitroheteroaryl-1,3,4-thiadiazole-2-yl)piperazinyl Derivatives to Predict New Similar Compounds as Antileishmanial Agents

  • ABDELLAH OUSAA1*, BOUHYA ELIDRISSI1, MOUNIR GHAMALI1, SAMIR CHTITA1, ADNANE AOUIDATE1, MOHAMMED BOUACHRINE2 and TAHAR LAKHLIFI1
  • 1Molecular Chemistry and Natural Substances Laboratory, Faculty of Science, Moulay Ismail University, Meknes, Morocco
    2MEM, ESTM, Moulay Ismail University, Meknes, Morocco
  • Abstract

    To search of newer and potent antileishmanial drugs, a series of 36 compounds of 5-(5-nitroheteroaryl-2-yl)-1,3,4-thiadiazole derivatives were subjected to a quantitative structure-activity relationship (QSAR) analysis for studying, interpreting and predicting activities and designing new compounds using several statistical tools, The multiple linear regression (MLR), non-linear regression (RNLM) and artificial neural network (ANN) models were developed using 30 molecules having pIC50 ranging from 3.155 to 5.046. The best generated MLR, RNLM and ANN models show conventional correlation coefficients R of 0.750, 0.782 and 0.967 as well as their leave-one-out cross-validation correlation coefficients RCV of 0.722, 0.744 and 0.720, respectively. The predictive ability of those models was evaluated by the external validation using a test set of 6 molecules with predicted correlation coefficients Rtest of 0.840, 0.850 and 0.802, respectively. The applicability domains of MLR and MNLR transparent models were investigated using William?s plot to detect outliers and outsides compounds. We expect that this study would be of great help in lead optimization for early drug discovery of new similar compounds.

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