Drug Discovery by Drug Repurposing: Combating COVID-19 in the 21st Century
Bibliographic record
Abstract
Due to the rapidly developing nature of the current COVID-19 outbreak and its almost immediate humanitarian and economic toll, coronavirus drug discovery efforts have largely focused on generating potential COVID-19 drug candidates as quickly as possible. Globally, scientists are working day and night to find the best possible solution to treat the deadly virus. During the first few months of 2020, the SARS-CoV-2 outbreak quickly developed into a pandemic, with a mortality rate that was increasing at an exponential rate day by day. As a result, scientists have turned to a drug repurposing approach to rediscover the potential use and benefits of existing approved drugs. Currently, there is no single drug approved by the U.S. Food and Drug Administration (FDA), for the treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, previously known as 2019-nCoV) that causes COVID-19. Based on only in-vitro studies, several active drugs are already in the clinical pipeline, made possible by following the compassionate use of medical protocols. This method of repurposing and the use of existing molecules like Remdesivir (GS-5734), Chloroquine, Hydroxychloroquine, etc. has proven to be a landmark in the field of drug rediscovery. In this review article, we will discuss the repurposing of medicines for treating the deadly novel coronavirus (SARS-CoV-2).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".