SpecReact: Role-Aware Spectrum-to-Spectrum Reaction Modeling with Unbalanced Optimal Transport

Chemical reactions are fundamental to functional molecule synthesis and play a central role in drug discovery, materials design, and chemical manufacturing. Spectra provide experimentally accessible and machine-readable observations of chemical transformations, motivating direct reaction modeling in the spectral domain without intermediate molecular-structure reconstruction. However, product spectrum prediction remains challenging because reactions contain an unordered and variable number of inputs with distinct chemical roles, while reactant-to-product spectra exhibit non-conservative changes such as peak shifts, intensity variations, and peak emergence or disappearance. We propose SpecReact, a spectrum-to-spectrum framework for predicting product spectra from reactant and reagent spectra. SpecReact employs a role-aware set encoder for variable-sized inputs and introduces an unbalanced optimal transport (UOT)-based spectral transformation module to model spectral correspondences and provide structured supervision for reaction-induced changes. We also construct a new spectrum-based reaction dataset from USPTO reaction records paired with simulated IR spectra. Experiments on simulated and experimentally measured spectra show that SpecReact consistently outperforms representative reaction baselines and remains robust to spectral perturbations. On experimental spectra dataset, SpecReact achieves a CosSim of $0.561$ and a peak F1 of $0.517$, demonstrating its effectiveness in practical spectroscopic scenarios. Our code can be found at https://anonymous.4open.science/r/SpecReact-A30C/