Inhibiteurs du SARS-CoV-2 : Relation Quantitative Structure-Activité (QSAR)

Abstract

In this work, we performed a QSAR study on a series of propiophenone-derived Anti-HIV used against SARS-CoV-2. Descriptors were obtained by Dragon software, from molecules optimized by the Gaussian-integrated B3LYP/6-31G(d, p) functional. A model based on multiple linear regression (MLR), was developed by Molegro for the prediction of pIC50s of the set of twenty inhibitors. The statistical treatment performed by Minitab shows that the statistical parameters obtained for the calibration and validation sets (correlation coefficients R=0.92, determination coefficients R2=0.84 and prediction coefficients Q2= 0.84) highlight the quality and relevance of the calculated model. Rigorous validation was considered to judge the stability and predictive capacity of this model. The goodness of fit of the developed model was verified by plotting the calculated values against the observed ones (R02 = 0.84).