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Updated: Oct 28, 2026

An Efficient Method for the Synthesis of Peptoids with Mixed Lysine-type/Arginine-type Monomers and Evaluation of Their Anti-leishmanial Activity
Published on: November 2, 2016
Structure-aware graph learning links peptide geometry to pathogen-specific antimicrobial potency
Eric Tang1, Zehua Sun2,3,4, Shuting Xu5,6
1School of Medicine, Monash University, Melbourne, VIC 3800, Australia.
Abstract:
Antimicrobial peptide screening requires both activity classification and pathogen-specific potency estimation. Here we present GeoMIC, a two-stage framework combining protein language model embeddings with predicted peptide geometry. GeoMIC achieved an AUROC of 0.844 on XUAMP and minimum inhibitory concentration (MIC) prediction correlations of 0.794 for Staphylococcus aureus and 0.797 for Escherichia coli. Capacity-matched ablations supported complementary contributions from predicted structure. Retrospective ranking recovered more known active peptides than the evaluated comparator, supporting further evaluation of GeoMIC for experimental candidate prioritization.
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