B-P.41: AMP-DiT: Antimicrobial Peptide Design with AMP-classifier Conditional Diffusion Transformers
Keywords
Antimicrobial peptides, Generative AI
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Antimicrobial resistance is a global health threat. Antimicrobial peptides (AMP) can help for their potent and promising ability to fight resistant pathogens. While AI is being employed to advance AMP discovery, deep learning methods remain limited by poor controllability, suboptimal sequence representations, and low experimental hit rates. We introduce AMP-DiT, a conditional discrete diffusion framework that generates AMPs directly in sequence space, avoiding reliance on continuous latent embeddings and large protein language model priors.
Our approach leverages a denoising diffusion transformer architecture with classifier guidance, enabling high fitness antimicrobial peptide properties during generation. Crucially, we condition the generative process on biologically meaningful signals derived from external AMP predictor, Macrel, explicitly biasing sampling toward sequences with high predicted antimicrobial activity. Since AMP activity is the primary determinant of downstream success, this conditioning serves as a strong inductive bias for generating functional peptides.
We observe that conditioning on a single AMP predictor not only improves scores on that model but also generalizes across other independent AMP prediction frameworks, suggesting that the model captures underlying antimicrobial features rather than overfitting to a specific predictor.
Unlike prior approaches that depend on protein language models trained on proteins, AMP-DiT is trained on peptide-specific data with guidance signals, reducing representation mismatch and avoiding biases inherited from global protein distributions. Overall, AMP-DiT establishes a framework for AMP generation that outperforms existing AMP design methods across key evaluation metrics. As we got 10 percent lower MIC in an AMP prediction model, meanwhile keeping diversity.
Co-authors: Attila Gürsoy, Ozlem Keskin
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