This invention discloses a method and
system for predicting
drug synergy based on cellular semantic-guided dynamic
biological pathway weights. This method integrates semantic priors from a large
language model, dynamic
biological pathway gating, and a triplet self-attention mechanism. It dynamically regulates
gene-pathway mapping relationships through cellular semantic features to form soft constraints, and applies biological hard constraints to network weights through a binary
mask matrix constructed from
gene-pathway subordination relationships. The combination of these two approaches, while adhering to the underlying facts of
molecular biology, introduces semantic soft gating to adapt to the heterogeneity of the
cellular microenvironment. On this basis, the complex interactions between drugs and cells are captured through a sequenced triplet self-attention mechanism, achieving accurate identification of
drug synergy and deep
coupling of cross-
modal information, thereby improving the biological
interpretability and generalization ability of the prediction.