AOP-DRL：抗酸化ペプチドの計算予測のための深層表現学習フレームワーク

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antioxidant peptide AOP-DRL is a deep representation learning framework for computational prediction. It enhances prediction accuracy and scalability for therapeutic and nutraceutical applications. This AI approach accelerates the discovery of therapeutic and nutraceutical peptides, offering a scalable and cost-effective alternative to traditional methods. The framework effectively handles variable peptide lengths and captures nonlinear residue interactions. It achieved accuracy improvements of 7.26%, 2.57%, 2.59%, and 4.20% on P60, P70, P80, and P90 subsets, respectively, demonstrating superior accuracy and generalization capabilities compared to state-of-the-art models across diverse datasets. The study aimed to develop a high-throughput, scalable, and cost-effective deep learning framework for predicting antioxidant peptides, overcoming limitations in current methods. The framework integrates protein language models and hierarchical convolutional networks, trained on experimentally validated antioxidant sequences and negative controls. Standard redox proteomics data partitioning protocols were employed for robust model evaluation. AOP-DRL provides a cost-effective and scalable alternative to experimental methods for identifying antioxidant peptides, accelerating the discovery of therapeutic and nutraceutical peptides. Its adaptability suggests potential for broader applications in bioactive peptide prediction for personalized medicine and functional foods. The area of science includes Biochemistry, Bioinformatics, and Artificial Intelligence.