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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 2113–2136 of 2336 models
Protein language models from 151M to 6.4B parameters, trained on over a billion sequences for sequence generation and zero-shot fitness prediction.
Protein motif-scaffolding driven by a protein language model instead of MSA pretraining, solving 22 of 24 benchmark problems with diverse backbones.
Chinese medical vision-language model pairing a Vision Transformer with an LLM to caption medical images and answer clinical questions in Chinese.
Fully 3D promptable segmentation foundation model for volumetric CT and MR, encoding whole volumes so anatomy can be segmented from one prompt point.
Chest X-ray vision-language model that generates free-text radiology reports, pairing a CXR-specific image encoder with a 7B LLaMA-2 language model.
Generative model of bacterial gene content that expands a handful of chosen KEGG modules into the full gene complement a viable cell would need.
Large-scale chest X-ray vision-language pretraining model that learns image-report alignment for zero-shot and few-shot radiograph classification.
Encoder-decoder framework unifying molecules, proteins, and natural language with SELFIES notation for cross-modal drug discovery tasks.
DNA language model for variant effect prediction across coding and non-coding regions, using whole-genome alignments of 100 vertebrate species.
Conditional GAN that generates small molecules against a protein-protein interaction interface, encoding the complex with graph attention networks.
MSA-free protein structure prediction that replaces multiple sequence alignments with a protein language model pre-trained on billions of sequences.
Multi-modal ophthalmic foundation model for generalist eye AI, spanning fundus imaging and OCT for disease screening, segmentation, and biomarkers.
De novo protein backbone generation by SE(3) flow matching, with motif-scaffolding built in. Samples a designable backbone in seconds on one GPU.
Structure-aware protein language model pairing amino acid tokens with Foldseek 3Di structural states, outperforming ESM-2 across 10 downstream tasks.
Abdominal CT segmentation model driven by CLIP text embeddings, covering 25 organs and 6 tumor types with zero-shot extension to new categories.
Interface residue accuracy estimation for protein complexes, predicting per-residue lDDT from whole-complex, per-monomer and cross-chain features.
Missense variant pathogenicity predictor built on AlphaFold 2 representations, scoring variants across the human proteome at 0.940 AuROC on ClinVar.
Codon-vocabulary protein language model that converts ProtBERT to 64 codon tokens via embedding seeding, masked pretraining, and distillation.
Vision-language pre-training framework for universal brain MRI diagnosis, learning from imaging-report pairs to cover more than ten brain diseases.
Self-supervised foundation model for retinal imaging, pretrained on 1.6 million unlabelled fundus and OCT scans to detect ocular and systemic disease.
fMRI foundation model pretrained with masked autoencoding on roughly 6,700 hours of recordings for clinical prediction and network discovery.
Discrete diffusion model for protein sequence and MSA generation, enabling controllable de novo design directly in sequence space without structure.