Comprehensive computational strategies for multi-target drug discovery in inflammatory bowel disease utilizing bioactive compounds
Bibliographic record
Abstract
Inflammatory bowel disease (IBD) is a chronic gastrointestinal condition that encompasses ulcerative colitis (UC) and Crohn's disease (CD). Targeting both inflammation and the epithelial barrier simultaneously can significantly improve symptom management in IBD, as a promising strategy. In this study, we focused on addressing both inflammation and the epithelial barrier. Until now, each therapeutic target including phosphodiesterase 4 (PDE4) and prolyl hydroxylase domain enzymes 1 and 2 (PHD1/2) have been studied separately. PDE4 plays a key role in the inflammatory process by converting cyclic AMP (cAMP) to AMP and its inhibition can suppress the production of inflammatory cytokines. Research has shown that inhibiting PHD1 and PHD2 increases levels of hypoxia-inducible factor-alpha (HIF-α), which in turn strengthens the epithelial barrier by promoting the expression of protective factors such as mucins and β-defensins. Through virtual screening, molecular docking, and molecular dynamics simulations, we identified five compounds-Cassiamin C, Ginkgetin, Hinokiflavone, Sciadopitysin, and Sojagol-as promising new drug candidates for IBD treatment. All compounds demonstrated superior free binding energy for the three targets compared to reference ligands, except Sojagol concerning PDE4B. Among these compounds, Ginkgetin was the best compound with potential ability of targeting multiple drug target proteins. Future experimental studies are warranted to validate these findings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".