Human endeavor for anti-SARS-CoV-2 pharmacotherapy: A major strategy to fight the pandemic
Bibliographic record
Abstract
The global spread of COVID-19 constitutes the most dangerous pandemic to emerge during the last one hundred years. About seventy-nine million infections and more than 1.7 million death have been reported to date, along with destruction of the global economy. With the uncertainty evolved by alarming level of genome mutations, coupled with likelihood of generating only a short lived immune response by the vaccine injections, the identification of antiviral drugs for direct therapy is the need of the hour. Strategies to inhibit virus infection and replication focus on targets such as the spike protein and non-structural proteins including the highly conserved RNA-dependent-RNA-polymerase, nucleotidyl-transferases, main protease and papain-like proteases. There is also an indirect option to target the host cell recognition systems such as angiotensin-converting enzyme 2 (ACE2), transmembrane protease, serine 2, host cell expressed CD147, and the host furin. A drug search strategy consensus in tandem with analysis of currently available information is extremely important for the rapid identification of anti-viral. An unprecedented display of cooperation among the scientific community regarding SARS-CoV-2 research has resulted in the accumulation of an enormous amount of literature that requires curation. Drug repurposing and drug combinations have drawn tremendous attention for rapid therapeutic application, while high throughput screening and virtual searches support de novo drug identification. Here, we examine how certain approved drugs targeting different viruses can play a role in combating this new virus and analyze how they demonstrate efficacy under clinical assessment. Suggestions on repurposing and de novo strategies are proposed to facilitate the fight against the COVID-19 pandemic.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".