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RIGR: Resonance-Invariant Graph Representation for Molecular Property Prediction

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RIGR improves molecular property prediction in cases of resonance, suggesting better consistency in outputs.

RIGR produces consistent predictions for molecules with resonance structures, enhancing the reliability of results.
Using 60% fewer features, RIGR shows comparable or superior performance to the Chemprop featurizer on prediction tasks.
The implementation utilizes a directed message-passing neural network to accurately represent molecular graphs.
RIGR serves as a versatile graph featurizer beyond just handling resonance, applicable across various molecular properties.

Zalte et al. (2025) studied this question.

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