Open drug discovery of anti-virals critical for Canada’s pandemic strategy
Bibliographic record
Abstract
In the event of the current COVID-19 pandemic and in preparation for future pandemics, open science can support mission-oriented research and development, as well as commercialization. Open science shares skills and resources across sectors; avoids duplication and provides the basis for rapid and effective validation due to full transparency. It is a strategy that can adjust quickly to reflect changing incentives and priorities, because it does not rely on any one actor or sector. While eschewing patents, it can ensure high-quality drugs, low pricing, and access through existing regulatory mechanisms. Open science practices and partnerships decrease transaction costs, increase diversity of actors, reduce overall costs, open new, higher-risk/higher-impact approaches to research, and provide entrepreneurs freedom to operate and freedom to innovate. We argue that it is time to re-open science, not only in its now restricted arena of fundamental research, but throughout clinical translation. Our model and attendant recommendations map onto a strategy to accelerate discovery of novel broad-spectrum anti-viral drugs and clinical trials of those drugs, from first-in-human safety-focused trials to late stage trials for efficacy. The goal is to ensure low-cost and rapid access, globally, and to ensure that Canadians do not pay a premium for drugs developed from Canadian science.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 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".