Discovery of Polyphenolic Compounds from Mangifera indica as PotentTherapeutics for Strongyloides stercoralis Infection via Computer-aidedDrug Design
Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: The global spread of Strongyloides stercoralis has escalated public health concerns, affecting over 600 million people worldwide. The rise in global migration has heightened the risk of transmission, underscoring the urgent need for effective treatment options. OBJECTIVE: This study aimed to investigate ten polyphenolic phytochemicals derived from Mangifera indica as potential alternatives to combat S. stercoralis. METHODS: The efficacy of these compounds was evaluated using computational techniques, including density functional theory (DFT) analysis, molecular docking, adsorption, distribution, metabolism, excretion, and toxicity (ADMET) assessment, and molecular dynamics (MD) simulations. RESULTS: DFT calculations revealed significant chemical reactivity in compounds such as kaempferol, ellagic acid, quercetin, norathyriol, mangiferin, and ferulic acid. Molecular docking identified mangiferin, quercetin, kaempferol, and norathyriol as top candidates for targeting S. stercoralis. A 200-ns MD simulation of the protein-ligand complex demonstrated the stability and binding behavior of these compounds compared to the reference drug, thiabendazole. ADMET screening confirmed their drug-likeness. Notably, quercetin and mangiferin exhibited strong binding affinities (ΔGbind = -42.35 and -54.57 kcal/mol, respectively), outperforming thiabendazole (ΔGbind = -28.94 kcal/mol). CONCLUSION: Quercetin and mangiferin emerge as promising alternatives to thiabendazole, offering favorable chemical reactivity, potent inhibition constants, and strong biological activity for the treatment of S. stercoralis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 it