High-throughput virtual screening of drug databanks for potential inhibitors of SARS-CoV-2 spike glycoprotein
Bibliographic record
Abstract
COVID-19, which is caused by a novel coronavirus known as SARS-CoV-2, has spread rapidly around the world, and it has infected more than 29 million individuals as recorded on 16 September 2020. Much effort has been made to stop the virus from spreading, and there are currently no approved pharmaceutical products to treat COVID-19. Here, we apply an in silico approach to investigate more than 3800 FDA approved drugs on the viral RBD S1-ACE2 interface as a target. The compounds were investigated through flexible ligand docking, ADME property calculations and protein–ligand interaction maps. Molecular dynamics (MD) simulations were also performed on eleven compounds to study the stability and the interactions of the protein–ligand complexes. The MD simulations show that bagrosin, chidamide, ebastine, indacaterol, regorafenib, salazosulfadimidine, silodosin and tasosartan are relatively stable near the C terminal domain (CTD1) of the S1 subunit of the viral S protein. The relative MMGBSA binding energies show that silodosin has the best binding to the target. The constant velocity steered molecular dynamics (SMD) simulations show that silodosin preferentially interacts with the RBD S1 and has potential to act as an interfering compound between viral spike–host ACE2 interactions. Communicated by Ramaswamy H. Sarma
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".