Advances and challenges in the prediction of atom transition maps.

Deep learning models for predicting atom transition maps in biochemical reactions can suffer from data leakage, and re-evaluation on a unified reaction test set highlights the severity of this problem.

• Why it matters: Accurate atom-level reaction representations are needed to connect large biochemical networks to complex phenotypes, support design-principle discovery, and enable flux estimation with labeling experiments.
• What they did: This systematic review categorizes and compares computational methods for atom transition maps at both reaction and network scales, emphasizing deep learning approaches and their combination with optimization-based methods.
• The result: The comparative analysis identifies open problems in generating atom transition maps and suggests future research directions to address leakage and improve prediction reliability.

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