Fragment‐Based Phenotypic Lead Discovery: Cell‐Based Assay to Target Leishmaniasis
Bibliographic record
Abstract
A rapid and practical approach for the discovery of new chemical matter for targeting pathogens and diseases is described. Fragment-based phenotypic lead discovery (FPLD) combines aspects of traditional fragment-based lead discovery (FBLD), which involves the screening of small-molecule fragment libraries to target specific proteins, with phenotypic lead discovery (PLD), which typically involves the screening of drug-like compounds in cell-based assays. To enable FPLD, a diverse library of fragments was first designed, assembled, and curated. This library of soluble, low-molecular-weight compounds was then pooled to expedite screening. Axenic cultures of Leishmania promastigotes were screened, and single hits were then tested for leishmanicidal activity against intracellular amastigote forms in infected murine bone-marrow-derived macrophages without evidence of toxicity toward mammalian cells. These studies demonstrate that FPLD can be a rapid and effective means to discover hits that can serve as leads for further medicinal chemistry purposes or as tool compounds for identifying known or novel targets.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".