Study of the Relationship Between Peptide Sequences and Antioxidant Activity Based on Logic Gates and Huffman Coding

Accurate identification of antioxidant peptides is crucial for the fields of drug discovery and functional food development. Nevertheless, the effective numerical representation of their variable‐length sequences remains challenging. To address this issue, a novel feature‐engineering framework is proposed, which is based on logic gates and Huffman coding. The development of two novel descriptors was initiated through the utilization of these methodologies. Subsequently, a robust composite descriptor was formulated by integrating these with classical one‐hot encoding. Predictive models constructed on this combined descriptor demonstrated excellent performance in identifying antioxidant peptides. A comparative analysis was conducted, which demonstrated the method's superior accuracy and significant performance enhancement over existing mainstream sequence characterization methods. These results validate the proposed framework as an effective and interpretable approach for deciphering the relationship between peptide sequences and their antioxidant activity.