Fragment-Based Phenotypic Lead Discovery To Identify New Drug Seeds That Target Infectious Diseases
Bibliographic record
Abstract
Fragment-based lead discovery has emerged over the last decades as one of the most powerful techniques for identifying starting chemical matter to target specific proteins or nucleic acids in vitro. However, the use of such low-molecular-weight fragment molecules in cell-based phenotypic assays has been historically avoided because of concerns that bioassays would be insufficiently sensitive to detect the limited potency expected for such small molecules and that the high concentrations required would likely implicate undesirable artifacts. Herein, we applied phenotype cell-based screens using a curated fragment library to identify inhibitors against a range of pathogens including Leishmania, Plasmodium falciparum, Neisseria, Mycobacterium, and flaviviruses. This proof-of-concept shows that fragment-based phenotypic lead discovery (FPLD) can serve as a promising complementary approach for tackling infectious diseases and other drug-discovery programs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".