GeneSyn-HGNN: Structured Context-Conditioned Interaction Learning for Drug Synergy Prediction

Learning the effect of an entity pair under a high-dimensional context requires structured entity representations and a scorer that captures pair-context dependence. Drug synergy prediction exemplifies this setting, as the same drug pair can exhibit different effects across cellular states. Directly modeling this dependence with an unrestricted interaction over two drug embeddings and raw gene expression, however, requires a large number of decoder coefficients. We introduce the Gene-aware Drug Synergy Hypergraph Neural Network (GeneSyn-HGNN), which integrates motif-guided multi-relational hypergraph encoding and a structured cell-conditioned decoder. Its central Gene-Synergy Kernel (GSK) models a parameter-efficient interaction between the gated drug pair and the encoded cell representation. Two complementary pathways support this interaction: Cell-Conditioned Gating (CCG) modulates both drug representations according to the encoded cell state, and Gene-Selective Attention (GSA) applies a gated-pair query to expression-scaled gene keys to produce a pair-dependent additive score. We analyze the decoder's expressiveness and coefficient complexity and derive a conditional decoder-level capacity bound under fixed, sample-independent upstream feature maps. Experiments on O'Neil and NCI-ALMANAC show that GeneSyn-HGNN achieves the highest listed AUC and AUPR values and maintains strong performance under reduced training fractions. These findings support structured cell conditioning as an effective and parameter-efficient approach to modeling drug-pair-cell interactions.