Discovery of Polyphenolic Compounds from Mangifera indica as PotentTherapeutics for Strongyloides stercoralis Infection via Computer-aidedDrug Design
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".