All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–10 of 10 filtered models
CryoACE
———Atomic protein model building from cryo-EM density maps, resolving conformational heterogeneity through atom-centric sampling and diffusion.
ProteinImaging38OpennessRNArefine
1——National University of Singapore +2 othersJune 29, 2026cryo_emgraph_neural_networkrepresentation_learning+2Atomic-level refinement of RNA 3D structures, using geometric attention networks to guide physics-based Monte Carlo sampling and L-BFGS optimization.
RNA32OpennessCryoDiff
———Uncertainty-aware diffusion model that enhances cryo-EM density maps while estimating voxel-wise confidence via Monte Carlo sampling.
Imaging20OpennessEmap2lig
2——Cryo-EM ligand modeling pipeline that detects bound ligand densities in a map, then reconstructs their atomic structures with a diffusion model.
ImagingSmall molecule25OpennessCryoProt
———Protein representation learning from cryo-EM density maps, transferring to flexibility, active-site, binding-affinity, and stability tasks.
ImagingProtein11OpennessBioimage restoration model pairing a NAFNet backbone with a perceptual GAN loss, best on LPIPS in 7 of 8 AI4Life microscopy benchmarks.
Imaging16OpennessEMReady2
13—Cryo-EM and cryo-ET map enhancement model that sharpens density maps with a Mamba-based dual-branch UNet and local resolution-guided learning.
Imaging54OpennessCryo-IEF
73——Cryo-EM foundation model pre-trained on 65 million particle images, enabling zero-shot classification, pose clustering, and quality assessment.
Imaging42OpennessCryoFM
35722Generative foundation model for cryo-EM density maps using flow matching, enabling zero-shot denoising, map sharpening, and missing wedge restoration.
Imaging77OpennessDistributional Graphormer
2.5K158—Deep learning framework predicting equilibrium distributions of molecular systems, enabling efficient ensemble generation and conformation sampling.
Protein46Openness