A global collaboration for systematic analysis of broad-ranging antibodies against the SARS-CoV-2 spike protein
Bibliographic record
Abstract
The Coronavirus Immunotherapeutic Consortium (CoVIC) conducted side-by-side comparisons of over 400 anti-SARS-CoV-2 spike therapeutic antibody candidates contributed by large and small companies as well as academic groups on multiple continents. Nine reference labs analyzed antibody features, including in vivo protection in a mouse model of infection, spike protein affinity, high-resolution epitope binning, ACE-2 binding blockage, structures, and neutralization of pseudovirus and authentic virus infection, to build a publicly accessible dataset in the database CoVIC-DB. High-throughput, high-resolution binning of CoVIC antibodies defines a broad and predictive landscape of antibody epitopes on the SARS-CoV-2 spike protein and identifies features associated with durable potency against multiple SARS-CoV-2 variants of concern and high in vivo efficacy. Results of the CoVIC studies provide a guide for selecting effective and durable antibody therapeutics and for immunogen design as well as providing a framework for rapid response to future viral disease outbreaks.
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 | 0.026 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".