All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
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Architectures
Learning Paradigms
Biological Subjects
Showing 1–16 of 16 filtered models
RFdiffusion3
Institute for Protein Design
All-atom diffusion model for de novo protein design conditioned on ligands, nucleic acids, and arbitrary non-protein atoms, enabling enzyme and DNA binder design.
RFdiffusion2
Institute for Protein Design
Atom-level generative diffusion model for de novo enzyme design. Scaffolds arbitrary functional group geometries, solving all 41 benchmark active sites vs. 16/41 for prior methods.
Pinal
Westlake University
A 16B-parameter framework for de novo protein design from natural language, converting text descriptions into functional protein sequences via two-stage structure-conditioned generation.
LigandMPNN
Institute for Protein Design
Protein sequence design method that explicitly models small molecules, nucleotides, and metals at atomic resolution, enabling ligand-aware design with 100+ validated designs.
NatureLM
Microsoft Research AI for Science
Unified science foundation model from Microsoft Research treating molecules, proteins, RNA, DNA, and materials as a shared sequence language for cross-domain generation.
ESM-3
EvolutionaryScale
Multimodal generative protein language model that jointly reasons over sequence, structure, and function. Trained at 98B parameters on 2.78 billion proteins.
AlphaFold 3
Google DeepMind
Unified diffusion-based model predicting structures of protein complexes with nucleic acids, small molecules, ions, and modified residues with atomic accuracy.
RoseTTAFold All-Atom
Baker Lab
Deep network that predicts structures of full biological assemblies containing proteins, nucleic acids, small molecules, metals, and covalent modifications simultaneously.
xTrimoPGLM
BioMap / Tsinghua University
Unified 100-billion-parameter protein language model combining autoencoding and autoregressive objectives for protein understanding and generation.
ProteinInvBench
A4Bio
Comprehensive NeurIPS 2023 benchmark for protein inverse folding, evaluating 8 models across single-chain, multi-chain, and de novo design tasks.
Chroma
Generate:Biomedicines
Generative diffusion model for programmable protein design that jointly samples novel structures and sequences, conditioned on symmetry, shape, and natural language.
ProGen2
Salesforce
Family of autoregressive protein language models (151M–6.4B parameters) trained on over a billion sequences for protein generation and zero-shot fitness prediction.
ABGNN
Huazhong University of Science and Technology / Microsoft Research
Graph neural network framework for antigen-specific antibody CDR design, combining a pre-trained antibody language model with one-shot sequence and structure generation.
RFdiffusion
Institute for Protein Design
Diffusion model for de novo protein design that generates novel backbone structures conditioned on binding targets, symmetry constraints, and functional motifs.
ProteinMPNN
Institute for Protein Design
Message passing neural network for fixed-backbone protein sequence design. Achieves 52.4% native sequence recovery, far surpassing Rosetta's 32.9%.
ProtGPT2
University of Bayreuth
Autoregressive protein language model based on GPT-2 that generates de novo protein sequences sampling unexplored regions of protein space.