A Comprehensive Benchmark for RNA 3D Structure-Function Modeling

Luis Wyss, Vincent Mallet, Wissam Karroucha, Karsten Borgwardt, Carlos Oliver
3/27/2025
q-bio.BMcs.LGstat.ML

No official code found. Best implementation: cgoliver/rnaglib (46 stars, MIT, last commit 2026-04-15).

Abstract

The relationship between RNA structure and function has recently attracted interest within the deep learning community, a trend expected to intensify as nucleic acid structure models advance. Despite this momentum, the lack of standardized, accessible benchmarks for applying deep learning to RNA 3D structures hinders progress. To this end, we introduce a collection of seven benchmarking datasets specifically designed to support RNA structure-function prediction. Built on top of the established Python package rnaglib, our library streamlines data distribution and encoding, provides tools for dataset splitting and evaluation, and offers a comprehensive, user-friendly environment for model comparison. The modular and reproducible design of our datasets encourages community contributions and enables rapid customization. To demonstrate the utility of our benchmarks, we report baseline results for all tasks using a relational graph neural network.

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Code Implementations(1)

cgoliver/rnaglibCommunity100%
MIT

Toolkit for structure-based deep learning on RNA.

468Shell, PythonOct 21, 20226 months agoMIT
bioinformaticsdeep-learningmlrnastructural-biology

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Cite this paper

@article{wyss2025comprehensive,
  title  = {A Comprehensive Benchmark for RNA 3D Structure-Function Modeling},
  author = {Luis Wyss and Vincent Mallet and Wissam Karroucha and Karsten Borgwardt and Carlos Oliver},
  year   = {2025},
  eprint = {2503.21681},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.21681v3}
}

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