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Correction: Coulter et al. OrgTRx: A Platform Developed in Queensland for the Extraction and Visualisation of Antimicrobial Susceptibility Data for the Surveillance of Resistance in Microorganisms. <i>Antibiotics</i> 2026, <i>15</i>, 63.

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Updated: Jan 29, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Leveraging Different Distance Functions to Predict Antiviral Peptides with Geometric Deep Learning from

Greneter Cordoves-Delgado¹, César R García-Jacas^2,3, Yovani Marrero-Ponce^4,5

¹Centro de Nanociencias y Nanotecnología, Universidad Nacional Autónoma de Mexico, Km. 107 Carretera Tijuana-Ensenada, Ensenada 22860, Baja California, Mexico.

Antibiotics (Basel, Switzerland)

|January 28, 2026

Summary

This summary is machine-generated.

Exploring alternative distance functions beyond Euclidean distance for peptide structure graphs significantly improves antiviral peptide prediction. This enhances machine learning models for drug discovery.

Keywords:

ESM-2 ESMFold QSAR antiviral peptides distance functions evolutionary scale modeling geometric deep learning graph deep learning

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Area of Science:

Computational chemistry
Bioinformatics
Drug discovery

Background:

Machine learning accelerates peptide-based drug discovery.
Graph learning frameworks utilize peptide structure graphs.
Current methods rely on Euclidean distance thresholds without strong evidence.

Purpose of the Study:

Investigate diverse distance functions for peptide structure graph generation.
Train deep graph learning models for antiviral peptide prediction using these graphs.

Main Methods:

Analyze amino acid closeness using various distance functions.
Compare derived graphs based on different distance metrics and random graphs.
Train and evaluate deep graph learning models with optimal graph representations.

Main Results:

Different distance functions create dissimilar graphs encoding distinct chemical spaces.
These alternative graphs improve the discriminative power of predictive models.
Performance comparisons with state-of-the-art models were conducted.

Conclusions:

Euclidean distance is insufficient for comprehensive peptide structure graph representation.
Employing varied distance functions yields superior graph structures.
Optimized graph representations lead to enhanced antiviral peptide prediction models.