Identification of Natural Products with Potential Activity against <em>Leishmania amazonensis </em>using computational models and experimental corroboration
Bibliographic record
Abstract
Leishmaniasis is one of the most important neglected tropical diseases according to the World Health Organization. The available drugs are expensive, not sufficiently effective, have serious cytotoxic effects and parasitic resistance has increased in the last years. In the present work, a virtual screening protocol was used to identify new natural compounds potentially active against Leishmania spp. using machine learning-based models. Three vegetable origin compounds were selected by using a multiclassifier composed by models developed with k-nearest neighbor, classification tree, Multilayer perceptron and Support Vector Machine; all these models for Leishmania amazonensis promastigote form were developed with WEKA software. The selected compounds showed in vitro activity against L. amazonensis (MHOM/BR/77/LTB0016) promastigotes with CI50 lower than 1 µg/mL using 96-well plates and resazurine fluorescence method.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".