Computational analysis of azadirachta indica-derived phytochemicals targeting α-amylase and α-glucosidase for antidiabetic activity
Bibliographic record
Abstract
• Identifies 24-methylene cycloartanol and gedunin as potent α-amylase and α-glucosidase inhibitors. • Employs molecular docking and ADMET analyses to highlight their binding and pharmacokinetic properties. • Demonstrates neem bioactives’ potential as safer, natural alternatives for diabetes treatment. • Reveals favorable drug-likeness and safety profiles, supporting further therapeutic development. • Opens pathways for clinical validation of Azadirachta indica in diabetes management. Azadirachta indica L. ( A. indica , neem), has long been used in the treatment of diabetes in numerous countries. This study aims to explore the molecular interactions of bioactive phytochemicals isolated from A. indica with two key enzymes involved in diabetes, human pancreatic α-amylase (4W93) and α-glucosidase (3CTT), using in silico approaches. Molecular docking was conducted with various ligands to evaluate their potential as inhibitors. Molecular docking analyses revealed that 24-methylene cycloartanol (24-MCL), stigmasterol (SML), and β-sitosterol (BSL) exhibited strong binding affinities (‒9.4 kcal/mol) to α-amylase, with SML forming one hydrogen bond (HB) along with multiple hydrophobic interactions. In contrast, montbretin A (MNA) showed a binding energy of ‒8.7 kcal/mol and formed several HBs. For α-glucosidase, gedunin (GN) showed the highest binding energy (‒9.1 kcal/mol), while SML exhibited a binding energy of ‒8.6 kcal/mol with one HB. Drug-likeness assessment using Lipinski’s Rule of 5 indicated that casuarine (CRE), catechin (CTN), and GN complied with the criteria without violations, while 24-MCL, SML, and BSL had one violation. Absorption, distribution, metabolism, excretion, and toxicity (ADMET) predictions highlighted favorable absorption profiles for 24-MCL, GN, and BSL, while MNA exhibited poor pharmacokinetic properties. Additionally, prediction of activity spectra for substances (PASS) prediction results further supported the antidiabetic potential of certain compounds. These findings warrant further in vivo and in vitro validation and clinical trials to assess 24-MCL and GN’s antidiabetic potential, while structural optimization could enhance potency for developing safer, more effective plant-based antidiabetic drugs.
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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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".