Refining Olfactory Receptor Representations from Binding Graph

Predicting which olfactory receptors a given odorant molecule activates is a central challenge in computational olfaction. Current methods treat each molecule-receptor pair in isolation, representing receptors with frozen embeddings from general-purpose protein language models. The measured response profile of a receptor across many odorants, which directly encodes its functional selectivity, is never explicitly incorporated into the receptor representation. We introduce Olfactory Binding Graph (OlfaGraph), a three-stage procedure that explicitly models this relational evidence. First, experimentally confirmed receptor responses and non-responses are organized into a signed bipartite graph over odorants and receptors. Second, a two-layer graph neural network propagates information across these relations to produce response-aware receptor embeddings that encode the binding evidence observed during training. Third, the adapted embeddings are combined with the original protein and molecular features in a downstream predictor that generalizes to entirely novel odorants. Across three datasets spanning mammalian and insect olfaction, OlfaGraph ranks among the strongest methods in both seen- and cold-molecule settings, using only 1.6 million trainable parameters. Ablations reveal a domain-specific limitation: general-purpose protein embeddings offer little advantage over far simpler descriptors. When general-purpose encoders fall short for a task-relevant protein family, the accumulated experimental receptor-odorant measurements themselves can ground a more effective representation. The code for OlfaGraph is available at https://anonymous.4open.science/r/Olfaction-Binding-5FE7.