Generic Vision and Cross-Attention for Reaction Yield Prediction

• URL: http://arxiv.org/abs/2608.00776v1
• Date: Sat, 01 Aug 2026 17:14:26 GMT
• Title: Generic Vision and Cross-Attention for Reaction Yield Prediction
• Abstract summary: Vision Cross-Attention architecture is proposed, fusing physical-organic data with 2D molecular topologies.<n>It is demonstrated that a generic computer vision backbone processing simple 2D skeletal structures independently outperforms purely quantum-based baselines.
• Score: 0.8092815324198175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional reaction yield prediction is constrained by 1D quantum descriptors that lack explicit spatial information. To address this gap, a dual-modal Vision Cross-Attention architecture is proposed, fusing tabular physical-organic data with 2D molecular topologies. Notably, it is demonstrated that a generic computer vision backbone processing simple 2D skeletal structures independently outperforms purely quantum-based baselines. By synergizing both modalities, superior predictive accuracy compared to traditional methodologies is achieved by the optimal cross-attention framework (Test RMSE = 5.27%). Through mechanistic probing, active, descriptor-guided spatial querying is observed, effectively offloading macroscopic steric identification to the visual pathway. Furthermore, a dynamic chemical hierarchy is learned by the network to heavily prioritize critical steric bottlenecks, such as the aryl halide. Concurrently, residual skip connections are utilized to protect non-spatial electronic parameters from destructive attenuation during fusion. Collectively, a scalable and highly interpretable blueprint is provided for augmenting physical chemistry with deep visual learning.

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