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Given the results of the Pfizer-developed inhibitor PF-07321332 in the treatment of the SARS-Covid-19 epidemic, we aimed to identify potential alternatives to this compound by utilizing various methods; we developed 2 D-QSAR models to predict the therapeutic activity of 78 analogues of PF-07321332, three statistical learning techniques including (MLP-ANN), (SVR), and (MLR) were exploited. Various validation approaches were applied to the three models developed following the use of five most relevant descriptors. The study of the characteristics of these descriptors proved that the inhibitory activity of PF-07321332 analogues is specifically affected by the structure of the molecule, its polarizability, and by the hydrogen bonds. The best model, named MLP-ANN (with a 5-3-1 architecture), was selected on the basis of the following statistical parameters:= 0.922,= 0.921. In addition, we performed a molecular docking and a molecular dynamics analysis of these compounds. The obtained results confirm that compound 8 can be a good alternative for compound PF-07321332. Communicated by Ramaswamy H. Sarma

2D-QSAR, docking, molecular dynamics, studies of PF-07321332 analogues to identify alternative inhibitors against 3CL pro enzyme in SARS-CoV disease