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applications.json
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{
"version": "1.0",
"last_updated": "2025-10-18",
"applications": [
{
"id": "boltz",
"name": "Boltz",
"version": "2025_09_05",
"category": "Protein Structure",
"description": "Protein structure prediction using deep learning",
"long_description": "Boltz is an advanced tool for predicting protein structures using state-of-the-art deep learning models. It leverages CUDA acceleration for fast inference and includes pre-downloaded model weights for immediate use.",
"base_image": "nvidia/cuda:12.1.1-cudnn8-runtime-ubuntu22.04",
"registry": "chiral.sakuracr.jp",
"image_path": "/boltz:2025_09_05",
"tags": ["protein", "ml", "gpu", "structure-prediction"],
"requirements": {
"gpu": true,
"memory_gb": 8,
"cuda_version": "12.1"
},
"documentation_url": "https://github.com/boltz-community/boltz"
},
{
"id": "gromacs",
"name": "GROMACS",
"version": "2025_09_05",
"category": "Molecular Dynamics",
"description": "Molecular dynamics simulation package",
"long_description": "GROMACS is a versatile package for molecular dynamics simulations, primarily designed for biochemical molecules. It provides high performance through GPU acceleration and optimized algorithms.",
"base_image": "nvcr.io/hpc/gromacs:2023.2",
"registry": "chiral.sakuracr.jp",
"image_path": "/gromacs:2025_09_05",
"tags": ["simulation", "molecular-dynamics", "hpc", "gpu"],
"requirements": {
"gpu": true,
"memory_gb": 4,
"cuda_version": "11.0"
},
"documentation_url": "https://www.gromacs.org"
},
{
"id": "blast",
"name": "BLAST",
"version": "2025_10_14",
"category": "Sequence Alignment",
"description": "Basic Local Alignment Search Tool",
"long_description": "BLAST finds regions of similarity between biological sequences. The program compares nucleotide or protein sequences to sequence databases and calculates the statistical significance of matches.",
"base_image": "ubuntu:22.04",
"registry": "chiral.sakuracr.jp",
"image_path": "/blast:2025_10_14",
"tags": ["alignment", "sequence", "bioinformatics"],
"requirements": {
"gpu": false,
"memory_gb": 2,
"cuda_version": null
},
"documentation_url": "https://blast.ncbi.nlm.nih.gov"
},
{
"id": "proteinmpnn",
"name": "ProteinMPNN",
"version": "2025_09_10",
"category": "Protein Design",
"description": "Message passing neural network for protein design",
"long_description": "ProteinMPNN is a deep learning method for computational protein design. It uses message passing neural networks to predict amino acid sequences that will fold into desired protein structures.",
"base_image": "nvidia/cuda:11.8.0-runtime-ubuntu22.04",
"registry": "chiral.sakuracr.jp",
"image_path": "/proteinmpnn:2025_09_10",
"tags": ["protein", "design", "ml", "gpu"],
"requirements": {
"gpu": true,
"memory_gb": 6,
"cuda_version": "11.8"
},
"documentation_url": "https://github.com/dauparas/ProteinMPNN"
},
{
"id": "rfdiffusion2",
"name": "RFdiffusion2",
"version": "2025_09_19",
"category": "Protein Design",
"description": "Diffusion-based protein structure generation",
"long_description": "RFdiffusion2 uses denoising diffusion probabilistic models to generate novel protein structures. It can design proteins with specific functional properties and structural motifs.",
"base_image": "nvidia/cuda:12.0.0-runtime-ubuntu22.04",
"registry": "chiral.sakuracr.jp",
"image_path": "/rfdiffusion2:2025_09_19",
"tags": ["protein", "diffusion", "generation", "gpu"],
"requirements": {
"gpu": true,
"memory_gb": 10,
"cuda_version": "12.0"
},
"documentation_url": "https://github.com/RosettaCommons/RFdiffusion"
},
{
"id": "ufold",
"name": "UFold",
"version": "2025_09_18",
"category": "RNA Structure",
"description": "RNA secondary structure prediction using deep learning",
"long_description": "UFold is a deep learning-based method for predicting RNA secondary structure from sequence. It uses a U-Net inspired architecture to directly predict base pairing probabilities.",
"base_image": "nvidia/cuda:11.3.0-runtime-ubuntu20.04",
"registry": "chiral.sakuracr.jp",
"image_path": "/ufold:2025_09_18",
"tags": ["rna", "structure", "ml", "gpu"],
"requirements": {
"gpu": true,
"memory_gb": 4,
"cuda_version": "11.3"
},
"documentation_url": "https://github.com/uci-cbcl/UFold"
},
{
"id": "intarna",
"name": "IntaRNA",
"version": "2025_09_18",
"category": "RNA Interaction",
"description": "RNA-RNA interaction prediction",
"long_description": "IntaRNA efficiently predicts RNA-RNA interactions, considering both hybridization and accessibility. It uses dynamic programming algorithms for accurate interaction site prediction.",
"base_image": "ubuntu:22.04",
"registry": "chiral.sakuracr.jp",
"image_path": "/intarna:2025_09_18",
"tags": ["rna", "interaction", "prediction"],
"requirements": {
"gpu": false,
"memory_gb": 2,
"cuda_version": null
},
"documentation_url": "https://github.com/BackofenLab/IntaRNA"
},
{
"id": "rnamigos2",
"name": "RNAmigos2",
"version": "2025_09_19",
"category": "RNA-Ligand",
"description": "RNA-small molecule binding prediction",
"long_description": "RNAmigos2 predicts RNA-small molecule interactions using graph neural networks. It enables virtual screening of compounds for RNA-targeted drug discovery.",
"base_image": "nvidia/cuda:11.6.0-runtime-ubuntu20.04",
"registry": "chiral.sakuracr.jp",
"image_path": "/rnamigos2:2025_09_19",
"tags": ["rna", "ligand", "ml", "gpu", "drug-discovery"],
"requirements": {
"gpu": true,
"memory_gb": 5,
"cuda_version": "11.6"
},
"documentation_url": "https://github.com/cgoliver/RNAmigos2"
},
{
"id": "freebindcraft",
"name": "FreeBindCraft",
"version": "2025_09_19",
"category": "Protein-Ligand",
"description": "Free energy calculation for protein-ligand binding",
"long_description": "FreeBindCraft performs accurate free energy calculations for protein-ligand complexes. It combines molecular dynamics simulations with advanced sampling techniques.",
"base_image": "nvidia/cuda:11.8.0-runtime-ubuntu22.04",
"registry": "chiral.sakuracr.jp",
"image_path": "/freebindcraft:2025_09_19",
"tags": ["protein", "ligand", "free-energy", "gpu"],
"requirements": {
"gpu": true,
"memory_gb": 8,
"cuda_version": "11.8"
},
"documentation_url": "https://github.com/example/freebindcraft"
},
{
"id": "rnahybrid",
"name": "RNAhybrid",
"version": "2025_10_14",
"category": "RNA Interaction",
"description": "MicroRNA target prediction tool",
"long_description": "RNAhybrid is a tool for finding the minimum free energy hybridization of a long and a short RNA. It is particularly useful for microRNA target prediction.",
"base_image": "ubuntu:22.04",
"registry": "chiral.sakuracr.jp",
"image_path": "/rnahybrid:2025_10_14",
"tags": ["rna", "mirna", "target-prediction"],
"requirements": {
"gpu": false,
"memory_gb": 1,
"cuda_version": null
},
"documentation_url": "https://bibiserv.cebitec.uni-bielefeld.de/rnahybrid"
}
]
}