Deciphering Allergen Peptides for Dermatological and Cosmetic Applications with Explainable Artificial Intelligence.

This study explores the potential of explainable artificial intelligence to advance our understanding of allergen peptides in the context of dermatology and cosmetics. We present a hybrid deep learning framework that integrates Temporal Convolutional Networks (TCN) and stacked Long Short-Term Memory (LSTM) architectures, enhanced with Evolutionary Scale Modeling (ESM) embeddings, to decode allergenic motifs embedded in peptide sequences. The ESM embeddings allow the model to capture both the evolutionary context and structural nuances of amino acids, enabling accurate classification of allergenic potential. Beyond classification, the framework emphasizes interpretability using state-of-the-art explainability tools such as Anchor, LIME, and SHAP. Anchor identifies minimal and decisive motifs responsible for allergenic activity, while LIME and SHAP provide a distributed importance map of k-mers, highlighting synergistic contributions across the peptide sequence. These methods ensure that the model output is not only precise, but it may also be biologically meaningful, opening a path to rational peptide modification strategies. In dermatology and cosmetic science, this approach represents a transformative tool, providing a scalable and transparent means to screen peptide-based formulations for allergenic risks, facilitates the design of safer bioactive peptides for therapeutic or cosmetic use, and supports regulatory compliance by grounding computational predictions in mechanistic explanations. Ultimately, the study bridges cutting-edge machine learning with dermatology and cosmetics, fostering the development of innovative, safe, and patient-centered cosmetic products.