Reaction Prediction via Interaction Modeling of Symmetric Difference Shingle Sets

📝 Original Info

• Title: Reaction Prediction via Interaction Modeling of Symmetric Difference Shingle Sets
• ArXiv ID: 2511.06356
• Date: 2025-11-09
• Authors: ** 논문에 명시된 저자 정보가 제공되지 않았습니다. (가능하면 원문에서 확인 후 추가) **

📝 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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