Strong absolute accuracy on mixture properties often masks poor recovery of non-ideal behavior, with large drops under strict molecule splits, making transfer to unseen molecules the central challenge.
Greenman, Yunsie Chung, Shih-Cheng Li, David E
2 Pith papers cite this work, alongside 524 external citations. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
MMGNN decomposes molecular graphs into multi-color subgraphs by atom-type pairs and applies shared message-passing per subgraph, achieving top macro AUC-ROC of 0.838 on classification and best RMSE on ESOL and FreeSolv among tested models.
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A Systematic Evaluation of Molecular Mixture Behavior Prediction
Strong absolute accuracy on mixture properties often masks poor recovery of non-ideal behavior, with large drops under strict molecule splits, making transfer to unseen molecules the central challenge.
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MMGNN: Multi-level, multi-color graph neural networks for molecular property prediction
MMGNN decomposes molecular graphs into multi-color subgraphs by atom-type pairs and applies shared message-passing per subgraph, achieving top macro AUC-ROC of 0.838 on classification and best RMSE on ESOL and FreeSolv among tested models.