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Abstract

We are in the half past of 2022, but still, we are facing the coronavirus pandemic situation. When a patient is hospitalized, only some FDAapproved drugs were administered to cure the patient. In treating coronavirus infection, nitazoxanide, granulocyte-macrophage colony-stimulating factor inhibitors, and various monoclonal antibodies are present. But all the molecules used in the treatment were not so effective in fully curing the patient. So, to break this jinx to develop of newer generation anti-SARS-CoV-2 drug molecules, computational approaches played an essential role. 2D QSAR studies related to anti-SARS-CoV-2 molecule development, some QSAR models observed with good statistical parameters such as R 2 : 0.748, cross-validated Q 2 (LOO): 0.628, external predicted R 2 : 0.723 and another model suggested with R 2 : 0.764, Q 2 : 0.627 and R m 2 : 0.610, Q 2 (F 1 ): 0.727, Q 2 (F 1 ): 0.652, MAE score: 0.127. We developed a new 2D QSAR model with a higher number of molecules and greater statistical parameters. A dataset of 84 anti-SARS-CoV2 molecules was obtained from literature followed by descriptor calculation PADEL software; the QSAR model was generated using the Modelability index, dataset pretreatment, division, MLR equation, validation, and Y randomization test. The model was pIC50 = -1.79268(+/-0.3652) +0.07995(+/-0.03551) naaaC -0.4051(+/-0.09672) nsssN -0.45945(+/-0.11025) SHsOH +1.23189(+/-0.28144) ETA_BetaP with R 2 and Q 2 values were 0.87028 and 0.70493 with MAE fitness score value: 0.14298. Atoms E-state and electronic features of the molecules directly related to anti-SARS-CoV-2 drug activity. It can be easily concluded that we want to develop a small molecule effective against SARS-CoV-2 disease in the near future.