Imra Aqeel

Imra Aqeel, Abdul Majid

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.

Imra Aqeel

The global pandemic due to emergence of COVID 19 has created the unrivaled public health crisis. It has huge morbidity rate never comprehended in the recent decades. Researchers have made many efforts to find the optimal solution of this pandemic. Progressively, drug repurposing is an emergent and powerful strategy with saving cost, time, and labor. Lacking of identified repurposed drug candidates against COVID 19 demands more efforts to explore the potential inhibitors for effective cure. In this study, we used the combination of molecular docking and machine learning regression approaches to explore the potential inhibitors for the treatment of COVID 19. We calculated the binding affinities of these drugs to multitarget proteins using molecular docking process. We perform the QSAR modeling by employing various machine learning regression approaches to identify the potential inhibitors against COVID 19. Our findings with best scores of R2 and RMSE demonstrated that our proposed Decision Tree Regression (DTR) model is the most appropriate model to explore the potential inhibitors. We proposed five novel promising inhibitors with their respective Zinc IDs ZINC (3873365, 85432544, 8214470, 85536956, and 261494640) within the range of -19.7 kcal/mol to -12.6 kcal/mol. We further analyzed the physiochemical and pharmacokinetic properties of these most potent inhibitors to examine their behavior. The analysis of these properties is the key factor to promote an effective cure for public health. Our work constructs an efficient structure with which to probe the potential inhibitors against COVID-19, creating the combination of molecular docking with machine learning regression approaches.

Imra Aqeel, Abdul Majid

The COVID-19 pandemic has initiated a global health emergency, with an exigent need for effective cure. Progressively, drug repurposing is emerging a promise solution as it saves the time, cost and labor. However, the number of drug candidates that have been identified as being repurposed for the treatment of COVID-19 are still insufficient, so more effective and thorough drug exploring strategies are required. In this study, we joint the molecular docking with machine learning regression approaches to find some prospective therapeutic candidates for COVID-19 treatment. We screened the 5903 approved drugs for their inhibition by targeting the main protease 3CL of SARS-CoV-2, which is responsible to replicate the virus. Molecular docking is used to calculate the binding affinities of these drugs to the main protease 3CL. We employed several machine learning regression approaches for QSAR modeling to find out some potential drugs with high binding affinities. Our outcomes demonstrated that the Decision Tree Regression (DTR) model with best scores of R2 and RMSE, is the most suitable model to explore the potential drugs. We shortlisted six favorable drugs. These drugs have novel repurposing potential, except for one antiviral ZINC203757351 compound that has already been identified in other studies. We further examined the physiochemical and pharmacokinetic properties of these most potent drugs and their best binding interaction to specific target protease 3CLpro. Our verdicts contribute to the larger goal of finding effective cures for COVID-19, which is an acute global health challenge. The outcomes of our study provide valuable insights into potential therapeutic candidates for COVID-19 treatment.