Computational Investigation of the Therapeutic Potential of Detarium senegalense in the Management of Erectile Dysfunction
Bibliographic record
Abstract
Erectile dysfunction (ED) is a multifactorial social problem affecting men worldwide. While phosphodiesterase type 5 inhibitors (PDE5) like sildenafil are commonly used, they often present side effects, underscoring the need for alternative therapies. Therefore, this study investigated the potential of phytochemicals from Detarium senegalense in the management of ED. A library of phytochemicals from Detarium senegalense was generated, prepared, and interacted with six key enzymes implicated in ED, including PDE5, using the Schrödinger Maestro suite. The results identified catechin, epicatechin, and gallic acid as the leading compounds with significant binding affinities for the targeted enzymes. Catechin and epicatechin (−9.877 and −11.408 kcal/mol, respectively) exhibited comparable binding affinities to sildenafil (−11.926 kcal/mol) on PDE5. The MD simulation results also revealed superior stability and ability to maintain interaction with key amino acids at the active site of PDE5 over the entire simulation period for these compounds. These compounds also demonstrated favorable ADMET profiles over sildenafil, including high gastrointestinal absorption and no violation of Lipinski’s rule, indicating good bioavailability and drug likeness. These findings suggest that flavonoids from Detarium senegalense, especially catechin and epicatechin, have potential in the management of ED by interacting with multiple targets involved in its pathogenesis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".