Antigen prediction method and apparatus, device, and storage medium

An antigen prediction apparatus comprising:

• at least one memory configured to store program code;
• and
• at least one processor configured to read the program code and operate as instructed by the program code, the program code comprising:
• input code configured to cause at least one of the at least one processor to input genetic information, sequence information, and three-dimensional structure features of an immune cell receptor into an antigen prediction model;
• feature extraction code configured to cause at least one of the at least one processor to perform, by the antigen prediction model, feature extraction on the genetic information and the sequence information to obtain genetic features and sequence features of the immune cell receptor;
• feature fusion code configured to cause at least one of the at least one processor to integrate, by the antigen prediction model, the genetic features, the sequence features, and the three-dimensional structure features to obtain receptor features of the immune cell receptor;
• and
• antigen prediction code configured to cause at least one of the at least one processor to perform, by the antigen prediction model, full connection and normalization on the receptor features to output a probability of the immune cell receptor being associated with each candidate antigen of a plurality of candidate antigens;
• and determining, based on the probability of the immune cell receptor being associated with each candidate of the plurality of candidate antigens, an antigen binding to the immune cell receptor from the plurality of candidate antigens.

A non-transitory computer-readable storage medium storing computer code which, when executed by at least one processor, causes the at least one processor to at least:

• input genetic information, sequence information, and three-dimensional structure features of an immune cell receptor into an antigen prediction model;
• perform, by the antigen prediction model, feature extraction on the genetic information and the sequence information to obtain genetic features and sequence features of the immune cell receptor;
• integrate, by the antigen prediction model, the genetic features, the sequence features, and the three-dimensional structure features to obtain receptor features of the immune cell receptor;
• perform, by the antigen prediction model, full connection and normalization on the receptor features to output a probability of the immune cell receptor being associated with each candidate antigen of a plurality of candidate antigens;
• and
• determine, based on the probability of the immune cell receptor being associated with each candidate of the plurality of candidate antigens, an antigen binding to the immune cell receptor from the plurality of candidate antigens.