Discovery of potent inhibitors for SARS-CoV-2's main protease by ligand-based/structure-based virtual screening, MD simulations, and binding energy calculations
Bibliographic record
Abstract
COVID-19 has caused lockdowns all over the world in early 2020, as a global pandemic. Both theoretical and experimental efforts are seeking to find an effective treatment to suppress the virus. In silico drug design can play a vital role in identifying promising drug candidates against COVID-19. Herein, we focused on the main protease of SARS-CoV-2 that has crucial biological functions in the virus. We performed a ligand-based virtual screening followed by a docking screening for testing approved drugs and bioactive compounds listed in the DrugBank and ChEMBL databases. The top 8 docking results were advanced to all-atom MD simulations to study the relative stability of the protein-ligand interactions. MD simulations support that the catalytic residue, His41, has a neutral side chain with a protonated delta position. An absolute binding energy (ΔG) of -42 kJ mol-1 for the protein-ligand (Mpro-N3) complex has been calculated using the potential-of-mean-force (geometrical) approach. Furthermore, the relative binding energies were computed for the top docking results. Our results suggest several promising approved and bioactive inhibitors of SARS-CoV-2 Mpro as follows: a bioactive compound, ChEMBL275592, which has the best MM/GBSA binding energy; the second-best compound, montelukast, is an approved drug used in the treatment of asthma and allergic rhinitis; the third-best compound, ChEMBL288347, is a bioactive compound. Bromocriptine and saquinavir are other approved drugs that also demonstrate stability in the active site of Mpro, albeit their relative binding energies are low compared to the N3 inhibitor. This study provides useful insights into de novo protein design and novel inhibitor development, which could reduce the cost and time required for the discovery of a potent drug to combat SARS-CoV-2.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".