All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–13 of 13 filtered models
RedNet
3——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
———A conditional encoder-decoder language model that designs RNA sequences under simultaneous secondary-structure, fixed-base, and coding constraints.
RNA63OpennessModular deep-learning framework for 3D-structure-based RNA sequence design, pairing a direct GNN predictor (SCRU-Seq) and a diffusion model (SCRU-Diff) built on self-contained RNA units.
RNA17OpennessInversePep
———Diffusion-based generative model for structure-based peptide inverse folding, pairing a geometric GNN encoder with a Transformer denoiser to design sequences for a target backbone.
Protein10OpennessMoMPNN
53—Property-driven protein inverse folding: a ProteinMPNN checkpoint aligned via multi-objective preference optimization to improve developability while preserving structural fidelity.
Protein34OpennessAtomPaint
———A full-atom SE(3)-equivariant diffusion model that inpaints binding interfaces to design proteins that bind DNA, RNA, and small molecules.
ProteinSmall molecule19OpennessHD-Prot
———A hybrid-diffusion protein language model that adds a continuous-token diffusion head to a discrete pLM for joint sequence-structure modeling.
Protein14OpennessTriFlow
———Structure-conditioned protein sequence design model combining a RoseTTAFold-like three-track architecture with discrete flow matching for fast, few-step inverse folding.
Protein69OpennessgRNAde
———MRC Laboratory of Molecular Biology +1 otherDecember 1, 2025de_novo_designgenerativegraph_neural_network+5Generative RNA inverse-design model that produces sequences conditioned on a target 3D backbone, secondary structure, and partial sequence constraints.
RNA98OpennessProteinInvBench
202——Comprehensive NeurIPS 2023 benchmark for protein inverse folding, evaluating 8 models across single-chain, multi-chain, and de novo design tasks.
Protein87OpennessMaskedProteinEnT
123—An equivariant graph transformer trained with masked language modeling on protein structure to learn contextual amino acid encodings for sequence design and interface modeling.
Protein52OpennessProteinMPNN
1.8K1.8K—Message passing neural network for fixed-backbone protein sequence design. Achieves 52.4% native sequence recovery, far surpassing Rosetta's 32.9%.
Protein85Openness