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Dirichlet flow matching for DNA sequence generation

Published on:

8 February 2024

Primary Category:

Biomolecules

Paper Authors:

Hannes Stark,

Bowen Jing,

Chenyu Wang,

Gabriele Corso,

Bonnie Berger,

Regina Barzilay,

Tommi Jaakkola

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Key Details

Introduces Dirichlet flow matching for DNA sequence generation

Avoids pathologies of linear flow matching on simplices

Implements guidance and distillation

Evaluated on complex DNA datasets

Outperforms baselines in generative metrics and sequence design

AI generated summary

Dirichlet flow matching for DNA sequence generation

This paper introduces a new generative modeling approach called Dirichlet flow matching to generate DNA sequences. It is based on transporting noise to data on a probability simplex using Dirichlet distributions. This avoids issues with linear flow matching on simplices. The model allows guidance and distillation. It is evaluated on complex DNA sequence datasets where it achieves superior performance over baselines in distributional similarity and sequence design tasks.

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