%0 Journal Article
%T Quantitative Structure-Activity Relationship Study of a Benzimidazole-Derived Series Inhibiting <i>Mycobacterium tuberculosis H37Rv</i>
%A Georges St¨¦phane Demb¨¦l¨¦
%A Mamadou Guy-Richard Kon¨¦
%A Fandia Konate
%A Doh Soro
%A Nahoss¨¦ Ziao
%J Computational Chemistry
%P 71-96
%@ 2332-5984
%D 2022
%I Scientific Research Publishing
%R 10.4236/cc.2022.102004
%X This work was carried out on a series of twenty-two (22) benzimidazole derivatives with inhibitory activities against <em>Mycobacterium tuberculosis H37Rv</em> by applying the Quantitative Structure-Activity Relationship (QSAR) method. The molecules were optimized at the level DFT/B3LYP/6-31 + G (d, p), to obtain the molecular descriptors. We used three statistical learning tools namely, the linear multiple regression (LMR) method, the nonlinear regression (NLMR) and the artificial neural network (ANN) method. These methods allowed us to obtain three (3) quantitative models from the quantum descriptors that are, chemical potential (<em>¦Ì</em>), polarizability (<em>¦Á</em>), bond length <em>l</em> (C = N), and lipophilicity. These models showed good statistical performance. Among these, the ANN has a significantly better predictive ability <em>R</em><sup><em>2</em></sup> = 0.9995; RMSE = 0.0149; <em>F</em> = 31879.0548. The external validation tests verify all the criteria of Tropsha <em>et al</em>. and Roy <em>et al</em>. Also, the internal validation tests show that the model has a very satisfactory internal predictive character and can be considered as robust. Moreover, the applicability range of this model determined from the levers shows that a prediction of the pMIC of the new benzimidazole derivatives is acceptable when its lever value is lower than 1.
%K &lt
%K i&gt
%K Mycobacterium tuberculosis H37Rv&lt
%K /i&gt
%K Benzimidazole Derivatives
%K QSAR
%K ANN
%K Applicability Domain
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=116469