RxnComp: Chemical Reaction Completion via Constrained Electron Redistribution Matrix Inference

Organic reaction data extracted from patents and scientific literature underpin a wide range of reaction understanding and prediction tasks. However, many recorded reactions include only major products while omitting byproducts, leaving reaction equations incomplete and often element-imbalanced. Recovering these missing species is important for constructing chemically complete reaction records, yet manual annotation is prohibitively expensive at scale. Existing completion methods largely rely on heuristic rules, while evaluation typically depends on exact matching against curated ground-truth completions, making it difficult to assess large-scale unlabeled reaction corpora or characterize candidate quality beyond reference recovery. To address these limitations, we formulate reaction completion as a conditional generation problem and introduce **RxnComp**, a constrained generative framework that completes missing byproducts through electron redistribution matrix modeling while explicitly enforcing chemical conservation during generation. We further develop a reference-free evaluation framework with complementary proxy metrics to assess the chemical validity and overall quality of generated completion candidates without requiring ground-truth annotations. Across benchmark datasets, RxnComp consistently produces more chemically consistent and higher-quality completion candidates than existing baselines, while maintaining competitive reference-based recovery performance. These results demonstrate the potential of constrained conditional generation for scalable and chemically grounded reaction data completion.