A deep learning model for RNA 3D structure prediction using diffusion and multi-modal embeddings. Developed for the
Stanford RNA 3D Folding Kaggle competition.
RNAIX is heavily insipired and builds upon
RibonanzaNet and
Protenix models.
Assumes that MSA alignments are precomputed.
1import torch
2from rnaix.model.model import RNAIX
3
4path_checkpoint = "../sample_model/model_v01.pt"
5device = "cuda" if torch.cuda.is_available() else "cpu"
6
7checkpoint = torch.load(path_checkpoint, map_location=device, weights_only=False)
8model = RNAIX(checkpoint["config"])
9model.load_state_dict(checkpoint["model_state_dict"])
10model.to(device)
11model.eval()
RNAIX was trained on the
Stanford RNA 3D Folding dataset, using only sequences with complete 3D coordinate annotations. Sequences with missing coordinates were excluded during training.
