新模型通过结构差异编码提升反应预测稳定性与准确性。
Reaction Prediction via Interaction Modeling of Symmetric Difference Shingle Sets
- 用对称差分词嵌入表示分子结构变化，消除顺序敏感性。
- 在扰动下平均提升8.76%的预测准确率，显著增强鲁棒性。
- 适合需要高稳定性的化学反应预测场景，如药物研发。
化学反应预测仍是有机化学中的核心挑战，现有机器学习模型存在两大关键局限：对输入排列（分子/原子顺序）敏感，且未能充分建模决定反应性的亚结构相互作用。这些缺陷导致预测结果不一致，泛化能力差。为此，我们提出ReaDISH模型，学习排列不变表示并引入交互感知特征。其创新包括：(1) 对称差分词嵌入，扩展差分反应指纹（DRFP），将词元表示为连续高维嵌入，捕捉结构变化同时消除顺序敏感性；(2) 几何-结构交互注意力机制，实现词元层面的分子内与分子间相互作用建模。大量实验表明，ReaDISH在多个基准测试中均提升反应预测性能，在排列扰动下平均提升8.76%的R²值。
原文摘要 · Abstract (English)
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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