Machine learning-guided optimization of triple agonist peptide therapeutics for metabolic disease.

Wong, Anthony et al.·Frontiers in bioinformatics·2025·
RPEP-141712025RETHINKTHC RESEARCH DATABASErethinkthc.com/research

Quick Facts

Study Type
Not classified
Evidence
Not graded
Sample
Not reported

What This Study Found

Key Numbers

How They Did This

Why This Research Matters

What This Study Doesn't Tell Us

Trust & Context

Original Title:
Machine learning-guided optimization of triple agonist peptide therapeutics for metabolic disease.
Published In:
Frontiers in bioinformatics, 5, 1687617 (2025)
Database ID:
RPEP-14171

Evidence Hierarchy

Meta-Analysis / Systematic Review
Randomized Controlled Trial
Cohort / Case-Control
Cross-Sectional / ObservationalSnapshot without intervening
This study
Case Report / Animal Study
What do these levels mean? →

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Cite This Study

RPEP-14171·https://rethinkpeptides.com/research/RPEP-14171

APA

Wong, Anthony; Guduri, Sanskruthi; Chen, TsungYen; Patel, Kunal. (2025). Machine learning-guided optimization of triple agonist peptide therapeutics for metabolic disease.. Frontiers in bioinformatics, 5, 1687617. https://doi.org/10.3389/fbinf.2025.1687617

MLA

Wong, Anthony, et al. "Machine learning-guided optimization of triple agonist peptide therapeutics for metabolic disease.." Frontiers in bioinformatics, 2025. https://doi.org/10.3389/fbinf.2025.1687617

RethinkPeptides

RethinkPeptides Research Database. "Machine learning-guided optimization of triple agonist pepti..." RPEP-14171. Retrieved from https://rethinkpeptides.com/research/wong-2025-machine-learningguided-optimization-of

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Study data sourced from PubMed, a service of the U.S. National Library of Medicine, National Institutes of Health.

This study breakdown was produced by the RethinkPeptides research team. We analyze and report published research findings without making health recommendations. All interpretations are based solely on the published abstract and study data.