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

ABSTRACT: 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 pr

SUBMITTER: Duy HA 

PROVIDER: S-EPMC12581612 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature