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Monte Carlo Inverse RNA Folding.

Tristan Cazenave1, Hamza Touzani2

  • 1LAMSADE, Université Paris Dauphine - PSL, CNRS, Paris, France. cazenave@lamsade.dauphine.fr.

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|September 23, 2024

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Summary
This summary is machine-generated.

We developed a transformer neural network to improve RNA sequence design for specific structures. This AI approach outperforms traditional methods in solving complex inverse RNA folding problems.

Keywords:
Monte Carlo searchRNATransformers

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Machine Learning

Background:

  • The inverse RNA folding problem is crucial for designing functional RNA molecules.
  • Existing methods often struggle with the complexity of predicting RNA structures from sequences.
  • Monte Carlo search algorithms offer a framework for optimizing sequence design.

Purpose of the Study:

  • To develop an AI-driven approach for solving the inverse RNA folding problem.
  • To leverage a transformer neural network for generating effective priors in RNA sequence design.
  • To evaluate the performance of the Generalized Nested Rollout Policy Adaptation (GNRPA) algorithm with a transformer-generated prior.

Main Methods:

  • Training a transformer neural network on the Rfam database for inverse RNA folding.
  • Utilizing the trained transformer to generate priors for Eterna100 puzzles.
  • Applying the Generalized Nested Rollout Policy Adaptation (GNRPA) algorithm with the transformer prior to solve RNA folding instances.
  • Comparing results against handcrafted heuristics.

    • Main Results:

    • The transformer neural network successfully generated priors for RNA folding problems.
    • GNRPA, guided by the transformer prior, effectively solved instances from the Eterna100 dataset.
    • The transformer-generated prior demonstrated superior performance compared to handcrafted heuristics.

    Conclusions:

    • AI-driven priors significantly enhance the performance of search algorithms like GNRPA for inverse RNA folding.
    • Transformer neural networks offer a powerful tool for advancing RNA sequence design.
    • This approach shows promise for designing novel RNA molecules with desired structures and functions.