@article{pmc2021711,
author={{Affi, Sopi Thomas and Ouattara, Baf¨¦tigu¨¦ and Demb¨¦l¨¦, Georges St¨¦phane and Kon¨¦, Mamadou Guy-Richard and Ziao, Nahoss¨¦},
title={Predictive Modeling of <i>Toxoplasma Gondii</i> Activity of a Series of Substituted Imidazole-Thiosemicarbazides Using Quantum Descriptors},
journal={Physics and Materials Chemistry},
volume={7},
number={1},
pages={1--13},
year={2021},
url={http://pubs.sciepub.com/pmc/7/1/1},
issn={2372-7101},
abstract={Quantitative Structure Activity Relationship (QSAR) study of <i>Toxoplasma gondii</i> was done on a series of twenty-five (25) imidazole-thiosemicarbazide molecules. In order to obtain molecular descriptors all these molecules were optimized at B3LYP/LanL2DZ level. This study was performed using the linear multiple regression (MLR), nonlinear regression (MNLR) and artificial neural network (ANN) methods. These statistical methods allow to find three (3) quantitative models. Quantum descriptors which such as energy gap (¦¤E), dipole moment (¦Ì), enthalpy of formation (¦¤fH), bond length (D(C-S)) and lipophilicity (Logp) were used in models elaboration. Among obtained models, RNA model has much better predictive ability than other models with <b>R</b><SUP><b>2</b></SUP> = 0.9291 and <b>RMCE =</b> 0.00023. A decrease in energy gap (¦¤E) which is the main descriptor could significantly improve the <i>Toxoplasma gondii</i> <b>IC</b><SUB><b>50</b></SUB> inhibitory concentration of substituted imidazole-thiosemicarbazide analogues. Furthermore, the external validation test <b>pIC</b><SUB><b>50</b></SUB> theo/<b> pIC</b><SUB><b>50</b></SUB> exp and the applicability domain from Cook's distance were verified.},
doi={10.12691/pmc-7-1-1}
publisher={Science and Education Publishing}
}