Front Cover: Fragment‐Based Phenotypic Lead Discovery: Cell‐Based Assay to Target Leishmaniasis (ChemMedChem 14/2018)
Bibliographic record
Abstract
The Front Cover shows a macrophage being attacked by Leishmania parasites. Fortunately, fragment small-molecule “Fragman” comes to the rescue by defeating the malicious parasites and thus saving the grateful macrophage. This illustrates that fragment-based phenotypic lead discovery (FPLD) can serve as a rapid and practical strategy to generate leads for a wide array of drug discovery programs. The FPLD strategy combines aspects of phenotypic screening and fragment-based lead discovery and takes advantage of cell-permeability properties of small molecules. Cover art by Alan Rossi and Yann Ayotte. More information can be found in the Full Paper by Albert Descoteaux, Steven R. LaPlante et al. on page 1377 in Issue 14, 2018 (DOI: 10.1002/cmdc.201800161).
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.073 |
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; both teacher heads agree on what is shown here.
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".