Structured Chemical Reaction Modeling with Multitask Graph Neural Networks

Maryam Astero, Anchen Li, Elena Casiraghi, Juho Rousu

Abstract: h3>Abstract</h3> <p>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 <i>multitask learning</i> 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.</p>

External IDs:doi:10.1101/2025.10.13.682169