Reaction Prediction via Interaction Modeling of Symmetric Difference Shingle Sets

Yang Yang (Nanjing University of Science and Technology) · Runhan Shi (Shanghai Jiao Tong University) · Letian Chen (Shanghai Jiaotong University) · Gufeng Yu (Shanghai Jiao Tong University)

benchmarkschemical reaction predictiongeometry-structure interaction attentioninter-molecular interactionsinteraction-aware featuresintra-molecular interactionsmachine learning modelsorder sensitivitypermutation-invariant representationsr$^2$reaction prediction performancereaction-specific structural changesrobustnesssubstructural interactionssymmetric difference shingle encoding

Chemical reaction prediction remains a fundamental challenge in organic chemistry, where existing machine learning models face two critical limitations: sensitivity to input permutations (molecule/atom orderings) and inadequate modeling of substructural interactions governing reactivity. These shortcomings lead to inconsistent predictions and poor generalization to real-world scenarios. To address these challenges, we propose ReaDISH, a novel reaction prediction model that learns permutation-invariant representations while incorporating interaction-aware features. It introduces two innovations: (1) symmetric difference shingle encoding, which computes molecular shingle differences to capture reaction-specific structural changes while eliminating order sensitivity; and (2) geometry-structure interaction attention, a mechanism that models intra- and inter-molecular interactions at the shingle level. Extensive experiments demonstrate that ReaDISH improves reaction prediction performance across diverse benchmarks. It shows enhanced robustness with an average improvement of 8.76\% on R$^2$ under permutation perturbations.