arXiv:2605.21420v1 Announce Type: new Abstract: Reaction condition recommendation sits immediately after retrosynthetic disconnection selection, and in practice, chemists require both accurate predictions and the precedents that justify them. We present HiRes (Hierarchical Reaction Representations), a retrieval-augmented condition recommendation system whose learned reaction space serves as both a classifier feature and an inspectable precedent memory. The model combines a graph encoder, transformation-aware cross-attention, multi-stream reaction fusion, and a k-NN retrieval layer. HiRes achie
The continuous advancements in AI, particularly in areas like graph encoders and attention mechanisms, enable more sophisticated and interpretable applications in scientific domains.
This development improves autonomous decision-making in chemical synthesis, making drug discovery and materials science more efficient and potentially leading to new breakthroughs.
Reaction condition recommendations can now be made with both high accuracy and clear, inspectable justifications, bridging the gap between AI prediction and human chemical intuition.
- · Pharmaceutical industry
- · Materials science
- · AI in chemistry startups
- · Chemical research labs
- · Traditional high-throughput screening methods
- · Laboratories with limited AI integration
Accelerated discovery of new molecules and chemical processes.
Reduced R&D costs and faster time-to-market for new drugs and materials.
Potential for fully autonomous chemical synthesis pathways driven by AI, transforming manufacturing and resource utilization.
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Read at arXiv cs.LG