MixScentNet: A Multiscale Graph-based Framework for Predicting Scent Mixture Perception

Predicting the olfactory perception of scent mixtures remains a fundamental challenge in computational neuroscience. Existing methods derive mixture-level features by encoding individual components with a single-molecule feature encoder pretrained on annotated olfactory data and aggregating these features via mean pooling, concatenation, or self-attention. This paradigm faces two critical limitations: the scarcity of annotated olfactory data and the inability to capture complex interactions among mixture components. We present MixScentNet, a multiscale graph neural network framework, to address these challenges with two key innovations. First, we introduce a self-supervised pretraining strategy that uses molecular graph structures with RDKit features to predict corresponding Mordred descriptors, enabling the model to learn rich physicochemical knowledge without relying on scarce annotated olfactory data. Second, we propose a novel mixture-as-graph paradigm to model the constituent molecules as nodes in the mixture-level graph, aligning with the fact that humans holistically perceive scent mixtures. We further process the graph via a graph attention network V2 (GATv2) to capture the high-order molecular interactions. MixScentNet differs from existing methods in terms of its pretraining strategy and ability to capture mixture-level features, achieving state-of-the-art performance on both mixture-level olfactory label prediction and perceptual distance estimation tasks. We also find that MixScentNet can reproduce olfactory white phenomena, indicating that the model enjoys clear interpretability grounded in psychophysics. The demo code is available in the Anonymous link \url{https://anonymous.4open.science/r/Odor-D2DB}