AI-Guided Discovery and Optimization of Antimicrobial Peptides Through Species-Aware Language Model
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Abstract
The rise of antibiotic-resistant bacteria drives an urgent need for novel antimicrobial agents. Antimicrobial peptides (AMPs) show promise solutions due to their multiple mechanisms of action and reduced propensity for resistance development. This study introduces LLAMP (Large Language model for AMP activity prediction), a target species-aware AI model that leverages pre-trained language models to predict minimum inhibitory concentration (MIC) values of AMPs. Analysis of attention values allowed us to pinpoint critical amino acid residues (e.g., Trp, Lys, and Phe). Our work demonstrates the potential of AI to expedite the discovery of peptide-based antibiotics to combat antibiotic resistance.
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Very cool study! One suggestion: the pseudo perplexity values still seem pretty high even after fine-tuning, which may indicate some degree of underfitting. This could be due to the relatively small size of the 35M ESM2 model. Have you considered trying a larger model (150M or 650M)? If fine-tuning a larger ESM2 model is computationally prohibitive, it might still be informative to compare against the zero-shot performance of a larger model to assess whether fine-tuning is necessary, or whether a larger baseline alone achieves comparable predictive results.
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