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 <40, and Boman index <0) with a Random Forest classifier trained on 30 biochemical descriptors. In vitro validation showed that 18.5% of generated peptides exhibited strong activity (MIC≤10 μg/mL), with 38.9% reaching MIC ≤50 μg/mL while maintaining key biochemical properties. Most validated novel peptides are narrow-spectrum AMP targeting E. coli. Wet-lab results also showed that highly active cationic-amphipathic AMPs are characterized by significantly low counts of tiny and small residues, suggesting that avoiding using these residues or limiting them to a maximum of 2 and 3, respectively, might improve the activity of AMP. Taking both antimicrobial activity and hemolytic toxicity into account, 9 peptides were identified as non-toxic and active AMP candidates. This explainable framework enables efficient AMP discovery under biochemical constraints and yields experimentally validated candidates with translational potential.