Reaction Prediction via Interaction Modeling of Symmetric Difference Shingle Sets

• URL: http://arxiv.org/abs/2511.06356v2
• Date: Sun, 16 Nov 2025 04:36:05 GMT
• Title: Reaction Prediction via Interaction Modeling of Symmetric Difference Shingle Sets
• Authors: Runhan Shi, Letian Chen, Gufeng Yu, Yang Yang,
• Abstract summary: We propose ReaDISH, a novel machine learning model for chemical reaction prediction.<n>It learns permutation-invariant representations while incorporating interaction-aware features.<n>It shows enhanced robustness with an average improvement of 8.76% on R$2$ under permutations.
• Score: 4.597922051722059
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 extends the differential reaction fingerprint (DRFP) by representing shingles as continuous high-dimensional embeddings, capturing 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.

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