Discovery of Antivirals Using Phage Display
Bibliographic record
Abstract
The latest coronavirus disease outbreak, COVID-19, has brought attention to viral infections which have posed serious health threats to humankind throughout history. The rapid global spread of COVID-19 is attributed to the increased human mobility of today's world, yet the threat of viral infections to global public health is expected to increase continuously in part due to increasing human-animal interface. Development of antiviral agents is crucial to combat both existing and novel viral infections. Recently, there is a growing interest in peptide/protein-based drug molecules. Antibodies are becoming especially predominant in the drug market. Indeed, in a remarkably short period, four antibody therapeutics were authorized for emergency use in COVID-19 treatment in the US, Russia, and India as of November 2020. Phage display has been one of the most widely used screening methods for peptide/antibody drug discovery. Several phage display-derived biologics are already in the market, and the expiration of intellectual property rights of phage-display antibody discovery platforms suggests an increment in antibody drugs in the near future. This review summarizes the most common phage display libraries used in antiviral discovery, highlights the approaches employed to enhance the antiviral potency of selected peptides/antibody fragments, and finally provides a discussion about the present status of the developed antivirals in clinic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".