All Competitors
Every biological foundation model, evaluated and ranked by the bio.rodeo team
Applications
Architectures
Learning Paradigms
Biological Subjects
Showing 1–9 of 9 filtered models
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.
PINNACLE
Harvard University
Geometric deep learning model generating context-aware protein representations across 156 cell-type contexts from a multi-organ single-cell atlas.
scPML
Shenzhen University
Pathway-based multi-view learning for cell type annotation from single-cell RNA-seq data, integrating biological pathway knowledge through graph neural networks.
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.
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.
ESM-GearNet
Mila
A joint sequence-structure representation learning framework combining ESM-2 protein language model embeddings with GearNet geometric graph neural networks.
GearNet
Mila / IBM Research
A geometric relational graph neural network that learns protein structure representations via geometry-aware message passing and self-supervised pretraining.
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%.