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Dirichlet Flow Matching with Applications to DNA Sequence Design.

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    We introduce Dirichlet flow matching for faster sequence generation, outperforming autoregressive models. This new method enables efficient, controllable generation of complex DNA sequences.

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    Area of Science:

    • Computational Biology
    • Machine Learning
    • Sequence Generation

    Background:

    • Autoregressive models dominate sequence generation but are slow.
    • Discrete diffusion or flow models offer potential for faster, controllable generation.
    • Existing methods like linear flow matching on the simplex have limitations.

    Purpose of the Study:

    • To develop a novel flow matching framework for discrete sequence generation.
    • To address limitations of existing methods for sequence generation on the simplex.
    • To enable faster and more controllable generation of complex sequences, particularly DNA.

    Main Methods:

    • Developed Dirichlet flow matching using mixtures of Dirichlet distributions for probability paths.
    • Derived a connection between mixture scores and flow vector fields for guidance.
    • Introduced distilled Dirichlet flow matching for efficient one-step generation.
    • Applied the framework to complex DNA sequence generation tasks.

    Main Results:

    • Dirichlet flow matching overcomes discontinuities and pathologies of linear flow matching.
    • Distilled Dirichlet flow matching achieves O(L) speedups compared to autoregressive models.
    • Demonstrated superior performance in distributional metrics for DNA sequence generation.
    • Showcased effectiveness in achieving desired design targets for generated DNA sequences.
    • Classifier-free guidance improved unconditional generation and design target satisfaction.

    Conclusions:

    • Dirichlet flow matching provides a robust and efficient framework for discrete sequence generation.
    • The method significantly enhances speed and controllability compared to autoregressive approaches.
    • This approach shows promise for applications in synthetic biology and bioinformatics requiring precise sequence design.