ANTIBODY LIBRARY CONSTRUCTION METHOD AND DEVICE BASED ON DEEP LEARNING

摘要

An antibody library construction method based on deep learning, comprising the steps of: obtaining a corresponding relation among antigen epitopes, antigen recognition regions and coding genes, and constructing a first database matching with the antigen epitopes, the antigen recognition regions and the coding genes; processing the antigen epitopes; carrying out clustering and characteristic extraction on the first database; and taking the multi-dimensional vector as the input of a 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 screening out 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 an existing gene sequence database Y so as to establish a secondary antibody library.