ArticleMethods in molecular biology (Clifton, N.J.)2025
gRNAde: A Geometric Deep Learning Pipeline for 3D RNA Inverse Design.
Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- RNA design: update on computational frameworks and programs for inverse RNA folding.Briefings in bioinformatics · 2026Review
- RNA sequence design and protein-DNA specificity prediction with NA-MPNN.bioRxiv : the preprint server for biology · 2025Article
- Comprehensive datasets for RNA design, machine learning, and beyond.Scientific reports · 2025Article
- DRAG: design RNAs as hierarchical graphs with reinforcement learning.Briefings in bioinformatics · 2025Article
- Incorporating mRNA therapeutics into biological treatments of hematologic malignancies.Frontiers in immunology · 2025Review
- R3Design: deep tertiary structure-based RNA sequence design and beyond.Briefings in bioinformatics · 2024Article
- Deep generative design of RNA aptamers using structural predictions.Nature computational science · 2024Article
- Machine Learning to Enhance Biophysical Models for Riboswitch Discovery.Wiley interdisciplinary reviews. RNAReview
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Abstract
Fundamental to the diverse biological functions of RNA are its 3D structure and conformational flexibility, which enable single sequences to adopt a variety of distinct 3D states. Currently, computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. In this tutorial, we present gRNAde, a geometric RNA design pipeline operating on sets of 3D RNA backbone structures to design sequences that explicitly account for RNA 3D structure and dynamics. gRNAde is a graph neural network that uses an SE (3) equivariant encoder-decoder framework for generating RNA sequences conditioned on backbone structures where the identities of the bases are unknown. We demonstrate the utility of gRNAde for fixed-backbone re-design of existing RNA structures of interest from the PDB, including riboswitches, aptamers, and ribozymes. gRNAde is more accurate in terms of native sequence recovery while being significantly faster compared to existing physics-based tools for 3D RNA inverse design, such as Rosetta.
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