The application discloses a mechanism-driven nanobody-
antigen binding prediction method and
system, relates to the technical field of bio-
information processing and
artificial intelligence, and inputs
amino acid sequences of nanobodies and antigens into a
prediction system for
processing, and a construction process of the
prediction system comprises the following steps: after an
encoder encodes the
amino acid sequences of the nanobodies and the
amino acid sequences of the antigens, global features and local features of respective complementarity determining regions are extracted and fused to obtain nanobody fusion features and
antigen fusion features, and the nanobody fusion features and the
antigen fusion features are subjected to global average
pooling to obtain an
encoder feature of the nanobodies and an
encoder feature of the antigens; a combination prediction model models the interaction between the encoder feature of the nanobodies and the encoder feature of the antigens through
Hadamard product modeling, generates an interaction feature, and predicts a combination probability based on the interaction feature. Through the
cooperative work of the mechanism-driven encoder and the
prediction system, high-precision and high-efficiency nanobody-
antigen binding prediction is realized.