Structured Chemical Reaction Modeling with Multitask Graph Neural Networks

Astero, M.; Li, A.; Casiraghi, E.; Rousu, J.

doi:10.1101/2025.10.13.682169

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bioRxiv · 10.1101/2025.10.13.682169

Structured Chemical Reaction Modeling with Multitask Graph Neural Networks

Astero, M.·Li, A.·Casiraghi, E.·Rousu, J.

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Abstract

Modeling chemical reactions requires connecting fine-grained atom-bond edits with broader semantic categories. Yet, most machine learning approaches model these aspects in isolation: atom mapping, reaction center identification, and reaction classification are treated as separate problems. This separation limits accuracy, interpretability, and generalization. In this work, we argue that multitask learning provides a natural and powerful framework for reaction modeling. By jointly predicting mappings, centers, and classes within a single graph neural network, models can leverage structural dependencies between tasks. We proposed MARCC (Mapping-Assisted Reaction Center and Classification), a multitask architecture that achieves state-of-the-art performance on the USPTO-50K benchmark. MARCC demonstrates that multitask supervision not only improves accuracy across all tasks but also provides a structured representation of reaction mechanisms.

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Astero, M., Li, A., Casiraghi, E., Rousu, J.. 2025-10-14. Structured Chemical Reaction Modeling with Multitask Graph Neural Networks. https://doi.org/10.1101/2025.10.13.682169

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