Identification of novel potential anti-diabetic candidates targeting human pancreatic α-amylase and human α-glycosidase: an exhaustive structure-based screening
Bibliographic record
Abstract
Diabetes is a major health issue that has reached alarming levels, affecting nearly half a billion people worldwide. It is a serious and long-term medical condition that has a major impact on the lives and well-being of individuals, families, and societies. Diabetes is among the top 10 diseases responsible for death among adults, with an expected increase to 10.2% (578 million) by 2030 and 10.9% (700 million) by 2045. Carbohydrates are absorbed into the body upon hydrolysis by human pancreatic α-amylase and other intestinal enzymes, such as human α-glucosidase. α-Amylase and α-glucosidase are well-validated therapeutic targets in the treatment of type II diabetes mellitus (T2DM) and play a vital role in modulating blood glucose levels after a meal. Herein, we report novel and diverse molecules identified as potential candidates that are predicted to have affinities for α-amylase and α-glucosidase. These molecules were identified via hierarchical multistep docking of small-molecule databases with estimated binding free energies. A Glide XP score cutoff of –8.0 kcal/mol was implemented to filter out non-potential molecules from the database. Four molecules, amb22034702, amb18105639, amb17153304, and amb9760832, were identified after an exhaustive computational study involving the evaluation of binding interactions and assessment of the pharmacokinetics and toxicity profiles. In-depth analysis of protein–ligand interactions was performed using a 100 ns molecular dynamics (MD) simulation to establish dynamic stability. Furthermore, MM-GBSA based binding free energies were computed for 1000 trajectory snapshots to ascertain the strong binding affinities of these molecules for α-amylase and α-glucosidase. The identified molecules can be considered as promising candidates for further drug development through necessary experimental assessments.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".