Predictive analysis, diagnosis of COVID-19 through computational screening and validation with spectro photometrical approach

OBJECTIVE: To develop Favipiravir, based predictive models of coronavirus disease 2019 (COVID-19) from small molecule databases such as PubChem, Drug Bank, Zinc Database, and literature. METHODS: High Throughput Virtual Screening (HTVS) using different computational screening methods is used to identify the target and lead molecules. CoMFA (Comparative Molecular Field Analysis) is a 3D-QSAR procedure depending on information from known dynamic atoms and eventually permits one to plan and anticipate exercises of particles. These two analysis is used to train predictive models. RESULTS: The predictive model achieved the highest accuracy score with a relatively small dataset size can be a subject of overfitting. Datasets with over 500 samples demonstrate an accuracy of about 85–95%, that can be considered as very good. CONCLUSIONS: From the result it is observed that Increasing level of potassium, sodium and nitrogen will lead to burst lipid bilayer membrane of virus which cause RNA replication rapidly. However, low level of sodium, potassium and nitrogen will help in the DNA polymerase inhibition and replication can be stopped. The best developed QSAR model in terms of the druggability and activity relation has been selected over the parent Favipiravir molecule for designing COVID-19 drugs may lead towards pharmaceutical development in future.