  SARS-COV-2 is a positive single-strand RNA-based macromolecule that has
caused the death of more than 6.3 million people since June 2022. Moreover, by
disturbing global supply chains through lockdown, the virus has indirectly
caused devastating damage to the global economy. It is vital to design and
develop drugs for this virus and its various variants. In this paper, we
developed an in-silico study-based hybrid framework to repurpose existing
therapeutic agents in finding drug-like bioactive molecules that would cure
Covid-19. We employed the Lipinski rules on the retrieved molecules from the
ChEMBL database and found 133 drug-likeness bioactive molecules against SARS
coronavirus 3CL Protease. Based on standard IC50, the dataset was divided into
three classes active, inactive, and intermediate. Our comparative analysis
demonstrated that the proposed Extra Tree Regressor (ETR) based QSAR model has
improved prediction results related to the bioactivity of chemical compounds as
compared to Gradient Boosting, XGBoost, Support Vector, Decision Tree, and
Random Forest based regressor models. ADMET analysis is carried out to identify
thirteen bioactive molecules with ChEMBL IDs 187460, 190743, 222234, 222628,
222735, 222769, 222840, 222893, 225515, 358279, 363535, 365134 and 426898.
These molecules are highly suitable drug candidates for SARS-COV-2 3CL
Protease. In the next step, the efficacy of bioactive molecules is computed in
terms of binding affinity using molecular docking and then shortlisted six
bioactive molecules with ChEMBL IDs 187460, 222769, 225515, 358279, 363535, and
365134. These molecules can be suitable drug candidates for SARS-COV-2. It is
anticipated that the pharmacologist/drug manufacturer would further investigate
these six molecules to find suitable drug candidates for SARS-COV-2. They can
adopt these promising compounds for their downstream drug development stages.

Biomolecules;Machine Learning