Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
gRNAde: Geometric Deep Learning for 3D RNA inverse design
Authors: Chaitanya Joshi, Arian Jamasb, Ramon Viñas, Charles Harris, Simon Mathis, Alex Morehead, Rishabh Anand, Pietro Lio
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On a single-state fixed backbone re-design benchmark of 14 RNA structures from the PDB identified by Das et al. (2010), g RNAde obtains higher native sequence recovery rates (56% on average) compared to Rosetta (45% on average), taking under a second to produce designs compared to the reported hours for Rosetta. We further demonstrate the utility of g RNAde on a new benchmark of multi-state design for structurally flexible RNAs, as well as zero-shot ranking of mutational fitness landscapes in a retrospective analysis of a recent ribozyme. Experimental wet lab validation on 10 different structured RNA backbones finds that g RNAde has a success rate of 50% at designing pseudoknotted RNA structures, a significant advance over 35% for Rosetta. |
| Researcher Affiliation | Collaboration | 1University of Cambridge, UK, 2Prescient Design, Genentech, Roche, 3EPFL, Switzerland, 3University of Missouri, USA 5National University of Singapore |
| Pseudocode | Yes | Listing 1: Pseudocode for multi-state GNN encoder layer. |
| Open Source Code | Yes | Open source code and tutorials are available at: github.com/chaitjo/geometric-rna-design |
| Open Datasets | Yes | We create a machine learning-ready dataset for RNA inverse design using RNASolo (Adamczyk et al., 2022), a novel repository of RNA 3D structures extracted from solo RNAs, protein-RNA complexes, and DNA-RNA hybrids in the PDB. |
| Dataset Splits | Yes | After clustering, we split the RNAs into training ( 4000 samples), validation and test sets (100 samples each) to evaluate two different design scenarios: |
| Hardware Specification | Yes | sampling 100+ designs in 1 second for an RNA of 60 nucleotides on an A100 GPU (<10 seconds on CPU)... approximate peak GPU usage for max. number of states = 1: 12GB, 3: 28GB, 5: 50GB on a single A100 with at most 3000 total nodes in a mini-batch). This research was partially supported by Google TPU Research Cloud and Cambridge Dawn Supercomputer Pioneer Project compute grants. |
| Software Dependencies | No | The paper mentions using "PyTorch Geometric (Fey & Lenssen, 2019)" but does not provide specific version numbers for this or any other software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | All models use 4 encoder and 4 decoder GVP-GNN layers, with 128 scalar/16 vector node features, 64 scalar/4 vector edge features, and drop out probability 0.5, resulting in 2,147,944 trainable parameters. All models are trained for a maximum of 50 epochs using the Adam optimiser with an initial learning rate of 0.0001, which is reduced by a factor 0.9 when validation performance plateaus with patience of 5 epochs. |