ArticleBiomedicines2021
Discovery through Machine Learning and Preclinical Validation of Novel Anti-Diabetic Peptides.
Article in Biomedicines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 21 citations in OpenAlex.
- An Artificial-Intelligence-Discovered Functional Ingredient, NRT_N0G5IJ, Derived fromNutrients · 2021Trial
- De novo generation and in silico screening of anti-diabetic peptide candidates via a deep learning-attention framework with physicochemical feature fusion.Scientific reports · 2026Article
- Therapeutic peptide development revolutionized: Harnessing the power of artificial intelligence for drug discovery.Heliyon · 2024Review
- New Developments in Pharmacological Treatment of Obesity and Type 2 Diabetes-Beyond and within GLP-1 Receptor Agonists.Biomedicines · 2024Review
- Recent Advances in the Discovery of Novel Drugs on Natural Molecules.Biomedicines · 2024Article
- Bioactive compounds for human and planetary health.Frontiers in nutrition · 2023Review
- Prediction, Discovery, and Characterization of Plant- and Food-Derived Health-Beneficial Bioactive Peptides.Nutrients · 2022Review
- Predicting High Blood Pressure Using DNA Methylome-Based Machine Learning Models.Biomedicines · 2022Article
- Artificial Intelligence in Functional Food Ingredient Discovery and Characterisation: A Focus on Bioactive Plant and Food Peptides.Frontiers in genetics · 2021Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While there have been significant advances in drug discovery for diabetes mellitus over the past couple of decades, there is an opportunity and need for improved therapies. While type 2 diabetic patients better manage their illness, many of the therapeutics in this area are peptide hormones with lengthy sequences and a molecular structure that makes them challenging and expensive to produce. Using machine learning, we present novel anti-diabetic peptides which are less than 16 amino acids in length, distinct from human signalling peptides. We validate the capacity of these peptides to stimulate glucose uptake and Glucose transporter type 4 (GLUT4) translocation in vitro. In obese insulin-resistant mice, predicted peptides significantly lower plasma glucose, reduce glycated haemoglobin and even improve hepatic steatosis when compared to treatments currently in use in a clinical setting. These unoptimised, linear peptides represent promising candidates for blood glucose regulation which require further evaluation. Further, this indicates that perhaps we have overlooked the class of natural short linear peptides, which usually come with an excellent safety profile, as therapeutic modalities.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.