Accurate structure-activity relationship prediction of antioxidant peptides using a multimodal deep learning framework

Antioxidant peptides (AOPs) have emerged as promising peptide agents due to their efficacy in counteracting oxidative stress-related diseases and their applicability in functional food and cosmetic industries. In this study, we developed a comprehensive quantitative structure-activity relationship (QSAR) utilizing a multimodal deep learning framework that integrates 6 sequence-based structure representations with stacking ensemble neural architectures-convolutional neural networks, bidirectional long short-term memory, and Transformer-to enhance predictive accuracy. Additionally, we employed a generative model to design novel AOP candidates, which were subsequently evaluated using the best-performing QSAR model. Remarkably, the stacking models using one-hot encoding achieved outstanding predictive metrics, with accuracy, AUROC, and AUPRC values surpassing 0.90, and the MCC above 0.80, demonstrating a highly accurate and robust QSAR model. SHAP analysis highlighted that proline, leucine, alanine, tyrosine, and glycine are the top five residues that positively influence antioxidant activity, whereas methionine, cysteine, tryptophan, asparagine, and threonine negatively impact antioxidant activity. Finally, 604 high-confidence AOPs were computationally identified. This study demonstrates that the multimodal framework improves the prediction accuracy, robustness, and interpretability of the AOP. It also enables the efficient discovery of high-potential AOPs, thereby offering a powerful pipeline for accelerating peptide discovery in pharmaceutical and functional applications.