Peptide VAE Amp

--- language: - en tags: - biology - bioinformatics - antimicrobial-peptides - vae - hyperbolic-geometry - protein-design license: other metrics: - spearmanr - pearsonr - mae --- # PeptideVAE: Hyperbolic Antimicrobial Peptide Predictor PeptideVAE is a Transformer-based Variational Autoencoder (VAE) designed for the analysis and design of antimicrobial peptides (AMPs). It leverages hyperbolic geometry (Poincaré ball) to learn a structured latent space where radial distance encodes antimicrobial potency (MIC). ## Model Description - **Architecture:** Transformer Encoder-Decoder with Hyperbolic Projection. - **Latent Space:** 16-dimensional Poincaré ball (curvature $c=1.0$). - **Input:** Peptide sequences (length 10-50 AA). - **Task:** Regression (Log10 MIC prediction) and Sequence Generation. ## Key Features - **Biological Grounding:** Stress-tested for sensitivity to physicochemical properties. Correctly predicts reduced activity when positive charge density is decreased (e.g., K $ ightarrow$ E mutation in LL-37). - **Hyperbolic Hierarchy:** Higher antimicrobial activity (lower MIC) correlates with specific radial positioning in the hyperbolic manifold. - **Multimodal Embedding:** Combines amino acid identity, 5-adic physicochemical groups, and standard AA properties. ## Performance (5-Fold Cross-Validation) | Metric | Mean Value | Max (Best Fold) | |--------|------------|-----------------| | Spearman $ ho$ | 0.633 | 0.760 | | Pearson $r$ | 0.618 | 0.749 | | MAE | 0.32 Log10 units | 0.25 | | Reconstruction | 100% | 100% | ## Usage ```python import torch from peptide_vae import PeptideVAE # Load model model = PeptideVAE(latent_dim=16, hidden_dim=64) checkpoint = torch.load("pytorch_model.bin", map_location="cpu") model.load_state_dict(checkpoint["model_state_dict"]) model.eval() # Encode sequence seq = "LLGDFFRKSKEKIGKEFKRIVQRIKDFLRNLVPRTES" with torch.no_grad(): out = model.encode([seq]) z_hyp = out['z_hyp'] mic_pred = model.predict_mic(z_hyp) print(f"Predicted Log10(MIC): {mic_pred.item():.4f}") ``` ## Citation If you use this model in your research, please cite: ```bibtex @software{peptide_vae_2026, author = {AI Whisperers}, title = {PeptideVAE: Hyperbolic Antimicrobial Peptide Predictor}, year = {2026}, url = {https://huggingface.co/ai-whisperers/peptide-vae-amp} } ```

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