RNAbpFlow training data and code
RNAbpFlow: Base pair-augmented SE(3)-flow matching for conditional RNA 3D structure generation This repository contains the code and resources for training, fine-tuning and inference of RNAbpFlow. Overview RNAbpFlow, a novel sequence- and base-pair-condition
RNAbpFlow: Base pair-augmented SE(3)-flow matching for conditional RNA 3D structure generation
This repository contains the code and resources for training, fine-tuning and inference of RNAbpFlow.
Overview
RNAbpFlow, a novel sequence- and base-pair-conditioned SE(3)-equivariant flow matching model for generating RNA 3D structural ensemble. Leveraging a nucleobase center representation, RNAbpFlow enables end-to-end generation of all-atom RNA structures without the explicit or implicit use of evolutionary information or homologous structural templates. RNAbpFlow is freely available at https://github.com/Bhattacharya-Lab/RNAbpFlow.
Training paradigms
In this repository, you will find the following training datasets and trained model checkpoints:
| Checkpoint Name | Parameters | Training Data | Date Cutoff | Fine-tuned |
| RNA3DB.ckpt | 16.9M | Representative chains from RNA3DB-provided training split | 2024-04-26 | No |
| CASP15.ckpt | 16.9M | Representative chains from entire RNA3DB set | 2022-04-26 | Yes |
| CASP16.ckpt | 16.9M | Representative chains from entire RNA3DB set | 2024-04-26 | Yes |
RNAbpFlow: Training and Fine-Tuning Guide
- Download the RNAbpFlow-Train.tar.gz folder and extract the files
- Use mamba to create a virtual environment and install dependencies for RNAbpFlow
conda install -n base -c conda-forge mamba mamba env create -f RNAbpFlow.yml - Activate the virtual environment
conda activate RNAbpFlow
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Files are hosted on the source repository. Click download to access the full dataset.