MIRRA: Fast, High-Fidelity Molecular Generation with Chemistry-Informed Discrete Flow Matching
We introduce MIRRA (Molecular Identity Representation, Refresh and Assembly), a discrete flow-matching generator that learns from chemical knowledge and evaluates identity probabilities only when needed, while keeping every identity in its vocabulary available.
ProofPaper ↗
Key points
- Fast, high-fidelity molecular generation requires accurate chemical choices and efficient sampling.
- Chemical building blocks reduce graph size, but make each node a choice among tens of thousands of related identities.
- Chemical features share information across related blocks, and learned identity corrections distinguish them in molecular context.
- On a held-out polymer test set, MIRRA achieves 100% periodic validity with known chain topology, versus 57.0% for a polymer variational autoencoder.
Sources (1)
- [1]MIRRA: Fast, High-Fidelity Molecular Generation with Chemistry-Informed Discrete Flow MatchingarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 06:10 PM
We introduce MIRRA (Molecular Identity Representation, Refresh and Assembly), a discrete flow-matching generator that learns from chemical knowledge and evaluates identity probabilities only when needed, while keeping every identity in its vocabulary available.
Fast, high-fidelity molecular generation requires accurate chemical choices and efficient sampling.
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