All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–24 of 61 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.
Boltz-2
MIT CSAIL / Recursion Pharmaceuticals
Open model that jointly predicts biomolecular structure and small-molecule binding affinity, approaching FEP+ accuracy in seconds on a single GPU.
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.
Protenix
ByteDance AI Lab
Open-source PyTorch reproduction of AlphaFold 3 (Apache 2.0) that matches or exceeds AF3 performance on protein-ligand, protein-protein, and protein-nucleic acid benchmarks.
Evolla
Westlake University
An 80B-parameter multimodal protein-language model that decodes protein function through natural language dialogue, integrating sequence, structure, and evolutionary context.
ProteinDT
UC Berkeley
A multimodal framework for text-guided protein design, enabling sequence generation, zero-shot editing, and property prediction via contrastive learning.
BioEmu-1
Microsoft
Generative deep learning model from Microsoft Research that emulates protein equilibrium ensembles at 100,000x the speed of molecular dynamics simulation.
ESM Cambrian
EvolutionaryScale
A family of protein language models (300M, 600M, 6B parameters) for representation learning that substantially outperforms ESM-2 at equivalent or smaller scale.
Boltz-1
MIT
Open-source deep learning model for biomolecular structure prediction achieving AlphaFold3-level accuracy, trained entirely on publicly available data.
SFM-Protein
Microsoft Research
A transformer protein language model using integrative co-evolutionary pre-training to capture both short-range and long-range residue interactions from sequence alone.
Chai-1
Chai Discovery
Multi-modal foundation model for biomolecular structure prediction covering proteins, small molecules, DNA, RNA, and glycans in a unified diffusion framework.
ProteinBench
ByteDance Research
A holistic evaluation framework for protein foundation models, assessing 25+ models across 8 tasks using four-dimensional metrics: quality, novelty, diversity, and robustness.
HelixFold3
Baidu PaddleHelix
Open-source reproduction of AlphaFold 3 from Baidu PaddleHelix, predicting structures of proteins, nucleic acids, and small molecule ligands with comparable accuracy.
SPIRED-Fitness
Tsinghua University
End-to-end framework predicting protein structure and mutational fitness from a single sequence, with 5x faster inference than ESMFold at comparable accuracy.
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.
Compute-Optimal PLM
BioMap
Scaling law study for protein language models that identifies compute-optimal training regimes for CLM and MLM architectures using 939M protein sequences.
Prot2Token
University of Missouri
A unified multi-task framework that converts diverse protein prediction tasks into autoregressive next-token prediction using pre-trained protein language model encoders.
ProTrek
Westlake University
Tri-modal protein language model unifying sequence, structure, and function via contrastive learning, enabling natural-language protein search across billions of entries.
OpenFold
Aqlaboratory
A trainable, open-source reimplementation of AlphaFold2 that matches its accuracy and runs 3-5x faster, enabling mechanistic research into protein structure learning.
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.
PLMSearch
Fudan University
Protein language model-based sequence search that detects remote homologs with threefold higher sensitivity than MMseqs2 at comparable speed.
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.