All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Applications
Architectures
Learning Paradigms
Biological Subjects
Showing 1–24 of 33 filtered models
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.
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.
BioEmu-1
Microsoft
Generative deep learning model from Microsoft Research that emulates protein equilibrium ensembles at 100,000x the speed of molecular dynamics simulation.
Boltz-1
MIT
Open-source deep learning model for biomolecular structure prediction achieving AlphaFold3-level accuracy, trained entirely on publicly available data.
RhoFold+
ml4bio
End-to-end RNA 3D structure prediction combining the RNA-FM language model with Invariant Point Attention, achieving SOTA on RNA-Puzzles and CASP15.
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.
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.
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.
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.
ERNIE-RNA
Tsinghua University
A structure-enhanced RNA language model that incorporates base-pairing constraints into self-attention, achieving state-of-the-art RNA structure and function prediction.
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.
RiNALMo
LBCB Sci
650M-parameter RNA language model pre-trained on 36M non-coding RNA sequences. Achieves state-of-the-art generalization on secondary structure prediction across unseen RNA families.
RNAformer
University of Freiburg
Axial-attention transformer for RNA secondary structure prediction from single sequences, without MSAs. Achieves state-of-the-art accuracy via homology-aware training.
ProteinINR
Kakao Brain
Multimodal protein pre-training framework that learns sequence, 3D structure, and surface representations jointly using implicit neural representations.
RNA-MSM
Peking University / Griffith University
Unsupervised RNA language model using multiple sequence alignments to predict secondary structure and solvent accessibility from evolutionary information.
xTrimoPGLM
BioMap / Tsinghua University
Unified 100-billion-parameter protein language model combining autoencoding and autoregressive objectives for protein understanding and generation.
ATOM-1
Atomic AI
RNA foundation model trained on chemical mapping data, achieving state-of-the-art accuracy in predicting RNA secondary structure, tertiary structure, and mRNA stability.
ProteinInvBench
A4Bio
Comprehensive NeurIPS 2023 benchmark for protein inverse folding, evaluating 8 models across single-chain, multi-chain, and de novo design tasks.
ProteinShake
BorgwardtLab
Python framework for building standardized protein structure datasets and benchmarks, with pre-processed data from PDB and AlphaFoldDB for deep learning evaluation.
Chroma
Generate:Biomedicines
Generative diffusion model for programmable protein design that jointly samples novel structures and sequences, conditioned on symmetry, shape, and natural language.