Combined docking methods and molecular dynamics to identify effective antiviral 2, 5-diaminobenzophenonederivatives against SARS-CoV-2
Bibliographic record
Abstract
The aim of this work is to contribute to the research in finding lead compounds for clinical use, to identify new drugs that target the SARS-CoV-2 virus main protease (Mpro). In this study, we used molecular docking strategies to analyze 2.5-diaminobenzophenone compounds against Malaria and to compare results with the Nelfinavir as a FDA-approved HIV-1 protease inhibitor recommended for the treatment of COVID-19. These efforts identified the potential compounds against SAR-COV-2 Mpro with the docking scores ranges from -6.1 to -7.75 kcal/mol, which exhibited better interactions than the Nelfinavir. Among thirty-six studied, compounds 20c, 24c, 30c, 34c, 35c and 36c showed the highest affinity and involved in forming hydrophobic interactions with Glu166, Thr24, Thr25, and Thr26 residues and forming H-bonding interactions with Gln189, Cys145, and His41residues. Pharmacokinetic properties and toxicity (ADMET) were also determined for identified compounds. This study result in the identification of two compounds 35 and 36 having high binding affinity, good pharmacokinetics properties and lowest toxicity. The structural stability and dynamics of lead compounds within the active site of 3CLpro was also examined using molecular dynamics (MD) simulation. Essential dynamics demonstrated that the two complexes remain stable during the entire duration of simulation. We have shown that these two lead molecules would have the potential to act as promising drug-candidates and would be of interest as starting point for designing compounds against the 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".