Jumping from Fragment to Drug via Smart Scaffolds
Bibliographic record
Abstract
Abstract A focused drug repurposing approach is described where an FDA‐approved drug is rationally selected for biological testing based on structural similarities to a fragment compound found to bind a target protein by an NMR screen. The approach is demonstrated by first screening a curated fragment library using 19F NMR to discover a quality binder to ACE2, the human receptor required for entry and infection by the SARS‐CoV‐2 virus. Based on this binder, a highly related scaffold was derived and used as a “smart scaffold” or template in a computer‐aided finger‐print search of a library of FDA‐approved or marketed drugs. The most interesting structural match involved the drug vortioxetine which was then experimentally shown by NMR spectroscopy to bind directly to human ACE2. Also, an ELISA assay showed that the drug inhibits the interaction of human ACE2 to the SARS‐CoV‐2 receptor‐binding‐domain (RBD). Moreover, our cell‐culture infectivity assay confirmed that vortioxetine is active against SARS‐CoV‐2 and inhibits viral replication. Thus, the use of “smart scaffolds” based on binders from fragment screens may have general utility for identifying candidates of FDA‐approved or marketed drugs as a rapid repurposing strategy. Similar approaches can be envisioned for other fields involving small‐molecule chemical applications.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".