MIRA: Interpretable Multiscale Prediction of Molecular Mixture Properties

The property of a molecular mixture depends on its constituents, their proportions, and how they behave together. However, existing methods suffer from two key limitations: (1) interaction learning approaches typically fuse all signals into a single latent embedding before prediction, limiting interpretability and obscuring the separation between component-level and mixed-level contributions; (2) physically motivated decompositions offer clearer structure but often depend on property-specific equations or assumptions, restricting their transferability. To address these limitations, we introduce Mixture Interaction and Residual Aggregation (MIRA), a unified property-agnostic framework for molecular mixture property prediction. MIRA performs prediction-level multi-scale decomposition by assigning explicit predictive roles to composition-weighted constituents, molecular interactions, and residual whole-mixture effects. This design separates component-wise, pairwise, and formulation-level contributions while preserving a common architecture across diverse properties. Across 11 CheMixHub tasks, MIRA achieves the lowest or tied-lowest mean test MAE at the reported precision, reducing five-fold MAE by up to 61.0%. It further generalizes to external mixture-property datasets, including surface tension and electrolyte-property prediction, demonstrating the broad applicability of its architectural design.