research
The Reaction Model Put Both Sides on One Graph
RxnCLF encodes reactants and products together so pretraining can learn the transformation rather than two disconnected molecular snapshots.
Summary
RxnCLF encodes reactants and products together so pretraining can learn the transformation rather than two disconnected molecular snapshots.
RxnCLF uses a condensed reaction graph and contrastive pretraining on 1.7 million Pistachio reactions. The representation captures reaction-center and side-chain context, then outperformed reported graph and sequence baselines after fine-tuning on public and proprietary yield-prediction sets. The author-reported results concern benchmark and high-throughput-experiment datasets; they do not establish laboratory yield for arbitrary new reactions.
Why it matters
RxnCLF encodes reactants and products together so pretraining can learn the transformation rather than two disconnected molecular snapshots.
Limits and context
- The author-reported results concern benchmark and high-throughput-experiment datasets; they do not establish laboratory yield for arbitrary new reactions.
Key claims
RxnCLF encodes reactants and products together so pretraining can learn the transformation rather than two disconnected molecular snapshots.
Qualification: The author-reported results concern benchmark and high-throughput-experiment datasets; they do not establish laboratory yield for arbitrary new reactions.
Evidence: source-2026-08-07-013
Sources
- arXiv preprint 2608.06259arXiv · primary research
Corrections
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