Evaluation of Inhibitory Activity In Silico of In-House Thiomorpholine Compounds between the ACE2 Receptor and S1 Subunit of SARS-CoV-2 Spike
Bibliographic record
Abstract
At the end of 2019, the world was struck by the COVID-19 pandemic, which resulted in dire repercussions of unimaginable proportions. From the beginning, the international scientific community employed several strategies to tackle the spread of this disease. Most notably, these consisted of the development of a COVID-19 vaccine and the discovery of antiviral agents through the repositioning of already known drugs with methods such as de novo design. Previously, methylthiomorphic compounds, designed by our group as antihypertensive agents, have been shown to display an affinity with the ACE2 (angiotensin converting enzyme) receptor, a key mechanism required for SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) entry into target cells. Therefore, the objective of this work consists of evaluating, in silico, the inhibitory activity of these compounds between the ACE2 receptor and the S1 subunit of the SARS-CoV-2 spike protein. Supported by the advances of different research groups on the structure of the coronavirus spike and the interaction of the latter with its receptor, ACE2, we carried out a computational study that examined the effect of in-house designed compounds on the inhibition of said interaction. Our results indicate that the polyphenol LQM322 is one of the candidates that should be considered as a possible anti-COVID-19 agent.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".