This invention relates to the field of
bioinformatics processing and discloses a method and
system for mining the structure-activity relationship (SMR) of porcine neurotrophic peptides based on sequence features. The method includes constructing an original sample index table and fusing multi-source production data, extracting
peptide sequence features to generate a
sequence feature matrix, constructing a sequence-process joint graph containing
peptide nodes and
process state nodes, training a structure-activity relationship graph neural network to mine SMR relationships, and deriving a
process control decision table based on a process response sample set generated by the network, thereby achieving
online optimization of the porcine neurotrophic
peptide preparation process. This invention solves the problem of SMR mining caused by the separation of process parameters, sequence information, and activity data, achieving accurate characterization of the synergistic effect of sequence and process and reverse optimization of process parameters, thus improving the targeted enrichment efficiency and bioactivity retention level of target neurotrophic peptides.