Finding New Hits in Neglected Disease Projects: Target or Phenotypic Based Screening?
Bibliographic record
Abstract
In this article, we discuss the merits of both target-based and phenotypic screening strategies to find starting points for drug discovery projects in neglected tropical disease including: human African trypanosomiasis, Chagas disease, leishmaniasis and malaria. Technological advances now mean that it is possible to undertake high quality screens against isolated molecular targets at considerable scale. However target selection is a minefield of potential issues and often molecules identified and developed as potent inhibitors of targets do not translate into actives against the whole parasite. The potential for rapid resistance development is also a key issue when tackling individual molecular targets. In phenotypic screening, compounds are screened against the whole organism, looking for activity without a priori knowledge of the target(s) being modulated. This approach brings the benefits of increased chances of efficacy and potentially slowed resistance development of a successful medicine but the lack of knowledge of the molecular target can make the optimisation process more challenging. Advances in screening technologies has now brought phenotypic approaches up to the scale attained by target-based approaches and we discuss opportunities for advances in this arena concluding that a robust drug discovery portfolio for such diseases should include both phenotypic and target-based approaches.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".