Biochemical-knowledge-driven machine learning pipeline for generating potent antimicrobial peptides
Abstract
The growing threat of antimicrobial resistance (AMR) necessitates the rapid discovery of novel antimicrobial peptides (AMPs) as alternative therapeutics. However, most computational approaches rely on binary AMP or non-AMP classification or permissive MIC thresholds (e.g. ≤128 μg/mL), offering limited biological interpretability and translational value. We present CVAE-BIO, a biochemical-knowledge-driven, multi-module pipeline for the discovery of AMPs targeting drug-resistant Escherichia coli as a model pathogen yet generalisable to other bacterial targets. The model integrates a conditional variational autoencoder (CVAE) constrained by key biochemical properties (MIC≤10 μg/mL, net charge > + 2, peptide length < 40 residues, instability index
Fields of Study & Subjects
Antimicrobial Peptides and Activities
Machine Learning in Bioinformatics
vaccines and immunoinformatics approaches
Publication details
- Journal / source
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Volume
- 27
- Issue
- 2
- Publication date
- 1 March 2026
- Language
- EN