AIMC Topic: Hypoglycemic Agents
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Machine learning driven diabetes care using predictive-prescriptive analytics for personalized medication prescription.
Scientific reports
Jul 23, 2025
The increasing prevalence of type 2 diabetes (T2D) is a significant health concern worldwide. Effective and personalized treatment strategies are essential for improving patient outcomes and reducing healthcare costs. Machine learning (ML) has the po...
BertADP: a fine-tuned protein language model for anti-diabetic peptide prediction.
BMC biology
Jul 15, 2025
BACKGROUND: Diabetes is a global metabolic disease that urgently calls for the development of new and effective therapeutic agents. Anti-diabetic peptides (ADPs) have emerged as a research hotspot due to their therapeutic potential and natural safety...
Research Gaps, Challenges, and Opportunities in Automated Insulin Delivery Systems.
Journal of diabetes science and technology
Jul 1, 2025
BACKGROUND: Since the discovery of the life-saving hormone insulin in 1921 by Dr Frederick Banting in 1921, there have been many critical discoveries and technical breakthroughs that have enabled people living with type 1 diabetes (T1D) to live longe...
In-silico guided identification and studies of potential FFAR4 agonists for type 2 diabetes mellitus therapy.
Expert opinion on drug discovery
Jun 28, 2025
BACKGROUND: The activation of free fatty acid receptor 4 (FFAR4) enhances insulin sensitivity and glucose uptake while mitigating inflammation. It is a promising therapeutic approach for managing type 2 diabetes mellitus (T2DM).
Chemical Properties-Based Deep Learning Models for Recommending Rational Daily Diet Combinations to Diabetics Through Large-Scale Virtual Screening of α-Glucosidase Dietary-Derived Inhibitors and Verified In Vitro.
Journal of agricultural and food chemistry
Jun 5, 2025
The lack of suitable chemical research methodologies has hindered the discovery of rational daily diet combinations from large-scale dietary-derived compounds. Three deep learning models based on chemical properties for α-glucosidase inhibitors (AGIs...
Enhancing Antidiabetic Drug Selection Using Transformers: Machine-Learning Model Development.
JMIR medical informatics
Jun 2, 2025
BACKGROUND: Diabetes affects millions worldwide. Primary care physicians provide a significant portion of care, and they often struggle with selecting appropriate medications.
Machine learning-driven discovery of bioactive peptides from duckweed (Lemnaceae) protein hydrolysates: Identification and experimental validation of 20 novel antihypertensive, antidiabetic, and/or antioxidant peptides.
Food chemistry
Mar 28, 2025
Duckweed, a sustainable, protein-rich aquatic plant, has recently emerged as a promising source of bioactive peptides. However, their identification remains limited and challenging in such complex mixtures. Following duckweed hydrolysis with pepsin, ...
Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia.
Frontiers in endocrinology
Mar 20, 2025
BACKGROUND: Medication adherence plays a crucial role in determining the health outcomes of patients, particularly those with chronic conditions like type 2 diabetes. Despite its significance, there is limited evidence regarding the use of machine le...
SERPINA3: A Novel Therapeutic Target for Diabetes-Related Cognitive Impairment Identified Through Integrated Machine Learning and Molecular Docking Analysis.
International journal of molecular sciences
Feb 24, 2025
Diabetes-related cognitive impairment (DCI) is a severe complication of type 2 diabetes mellitus (T2DM), with limited understanding of its molecular mechanisms hindering effective therapeutic development. This study identified SERPINA3 as a potential...
Food-derived DPP4 inhibitors: Drug discovery based on high-throughput virtual screening and deep learning.
Food chemistry
Feb 21, 2025
Dipeptidyl peptidase-4 (DPP-4) is a critical target for the treatment of type 2 diabetes. This study outlines the development of six compounds derived from food sources and modified to create promising candidates for the treatment of diabetes. These ...
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