Computer-aided drug design to generate a unique antibiotic family
Bibliographic record
Abstract
The World Health Organization has identified antibiotic resistance as one of the three greatest threats to human health. The need for antibiotics is a pressing matter that requires immediate attention. Here, computer-aided drug design is used to develop a structurally unique antibiotic family targeting holo-acyl carrier protein synthase (AcpS). AcpS is a highly conserved enzyme essential for bacterial survival that catalyzes the first step in lipid synthesis. To the best of our knowledge, there are no current antibiotics targeting AcpS making this drug development program of high interest. We synthesize a library of > 700 novel compounds targeting AcpS, from which 33 inhibit bacterial growth in vitro at ≤ 2 μg/mL. We demonstrate that compounds from this class have stand-alone activity against a broad spectrum of Gram-positive organisms and synergize with colistin to enable coverage of Gram-negative species. We demonstrate efficacy against clinically relevant multi-drug resistant strains in vitro and in animal models of infection in vivo including a difficult-to-treat ischemic infection exemplified by diabetic foot ulcer infections in humans. This antibiotic family could form the basis for several multi-drug-resistant antimicrobial programs.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".