All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–18 of 18 filtered models
HBDesigner
16——Message-passing neural network that designs buried hydrogen-bond networks onto protein backbones, combining learned placement with PyRosetta scoring.
Protein60OpennessRedNet
4——Toyota Technological Institute at ChicagoMay 13, 2026generativegraph_neural_networkinverse_folding+3Multiscale graph neural network for fixed-backbone protein binder sequence design with a contrastive decoding algorithm to improve target selectivity.
Protein83OpennessGoForth
———RNA inverse-folding language model that designs nucleotide sequences satisfying a target secondary structure, fixed bases, and coding constraints.
RNA63OpennessRNA inverse folding framework pairing a graph neural network predictor with a diffusion model, designing sequences from self-contained RNA units.
RNA17OpennessInversePep
———Diffusion generative model for structure-based peptide inverse folding, pairing a geometric GNN encoder with a Transformer denoiser.
Protein10OpennessMoMPNN
63—Protein inverse folding model aligning ProteinMPNN by multi-objective preference optimization to improve developability without losing fold fidelity.
Protein34OpennessAtomPaint
———Full-atom SE(3)-equivariant diffusion model that inpaints binding interfaces to design proteins that bind DNA, RNA, and small molecules.
ProteinSmall molecule19OpennessHD-Prot
74—Multimodal protein language model that adds a continuous-token diffusion head to a discrete pLM, modeling structure without vector quantization.
Protein14OpennessTriFlow
9——Structure-conditioned protein sequence design, pairing a three-track architecture with discrete flow matching for fast, few-step inverse folding.
Protein69OpennessgRNAde
310321MRC Laboratory of Molecular Biology +1 otherDecember 1, 2025de_novo_designgenerativegraph_neural_network+5RNA inverse-folding model that generates sequences predicted to fold into a target 3D backbone, capturing non-canonical pairs and tertiary motifs.
RNA98OpennessRadDiff
———Retrieval-augmented diffusion model for protein inverse folding that conditions sequence generation on profiles from structurally similar homologs.
Protein27OpennessPRISM
—6—Carnegie Mellon University +2 othersOctober 13, 2025graph_neural_networkinverse_foldingprotein_design+3Retrieval-augmented inverse folding model that fuses structural motif retrieval with a hybrid attention decoder to design sequences for a backbone.
Protein20OpennessCaliby
1075—Potts-model inverse folding that conditions on a structural ensemble rather than a single backbone, improving designability and self-consistency.
Protein72OpennessBC-Design
213—Biochemistry-aware inverse folding model that augments backbone geometry with physicochemical point clouds, reaching ~90% sequence recovery on CATH.
Protein75OpennessMaskedProteinEnT
123—Structure-conditioned graph transformer trained with masked language modeling to learn residue encodings for inverse folding and antibody design.
Protein52OpennessProteinMPNN
1.8K1.9K—Message passing neural network for fixed-backbone protein sequence design. Achieves 52.4% native sequence recovery, far surpassing Rosetta's 32.9%.
Protein85Openness