Antibody library construction method and device based on deep learning

An antibody library construction device based on deep learning, comprising a construction module, a model training module, and a screening module, wherein

• the construction module is used for obtaining the corresponding relation among the antigen epitopes, the antigen recognition regions and the coding genes, and constructing the first database matching with the antigen epitopes, the antigen recognition regions and the coding genes;
• the model training module is used for processing the antigen epitopes by using the trained neural network model so as to obtain the coding gene sequence set X of the antibodies to be predicted;
• the trained neural network model is obtained by training according to the a method which comprises the steps of:
• carrying out clustering and characteristic extraction on the first database sequentially according to the classification of the antigens, the homology of the amino acid residues in the antigen epitopes, and the positions of the antigen recognition areas to obtain the multi-dimensional vector for predicting the antibody genes;
• and taking the multi-dimensional vector as the input of the temporal convolutional neural network, and stopping training until the error is lower than the threshold and tends to be stable to obtain the trained neural network model;
• and
• the screening module is used for screening out the antibody sequences having different activities, stability and specificity to the antigens in the coding gene sequence set X according to molecular docking, molecular dynamics and the existing gene sequence database Y so as to establish the secondary antibody library.