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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1081–1104 of 2565 models
Site-specific structure prediction conditioning AlphaFold3 diffusion on a fixed receptor and known binding pocket. 81.2% success on PoseBusters V2.
Cross-species brain spatial transcriptomics foundation model pretrained on 133M cells from human, macaque, marmoset, and mouse whole brains.
Antimicrobial discovery model predicting compound potency against unseen bacterial strains and generating de novo antibiotics from pathogen genomes.
De novo design of heavy metal-binding peptides by classifier-guided diffusion over ESM-2 embeddings, with Cu and Zn binders validated in vitro.
Zebrafish single-cell foundation model built on the Geneformer framework, producing frozen gene and cell embeddings for developmental analysis.
Spatial proteomics imputation model inferring surface protein abundance from transcriptomics-only tissue sections via dual graph attention networks.
Open medical multimodal models from Google, built on Gemma 3 with a medically tuned SigLIP vision encoder for clinical text and image understanding.
Histopathology foundation model trained with direct slide-level supervision on 37k whole-slide images. Averages 0.784 AUROC on 10 biomarker tasks.
Single-cell transcriptomics foundation model that encodes gene regulatory network structure into self-attention through graph signal processing.
Structure-based 3D molecule generation with one diffusion backbone for fragment growing, linker design, scaffold hopping, and side-chain decoration.
Medically tuned SigLIP encoder from Google that maps medical images and text into one embedding space for zero-shot classification and retrieval.
Multimodal single-cell foundation model pretrained on 4M+ co-assayed cells, predicting 382 surface proteins from transcriptomes alone, zero-shot.
Peptide toxicity classification that fuses ESM-2 embeddings with an ESMFold-predicted residue contact graph read by a graph transformer.
Whole-slide histopathology foundation model trained end-to-end on slide-level labels across 18 tasks, on 5% of the energy of SSL-trained peers.
Spatial transcriptomics foundation model pretrained on 22 million cells, encoding each cell with its neighbors for niche and density prediction.
Mutational effect predictor for protein-protein binding energy, matching the FoldX force field's accuracy with a 1,000x speed-up.
Gradient-free protein design framework that treats engineering as Monte Carlo sampling of a user-defined energy landscape over pretrained models.
De novo protein binder design that conditions AlphaFold2-Multimer hallucination on a chosen fold, from TIM barrels to VHH nanobody scaffolds.
De novo protein backbone generator trained on low-confidence AlphaFold structures as corrupted data, reaching 86% designability at 700 residues.
Multimodal all-atom generative model for zero-shot de novo antibody and protein-binder design, validated by wet-lab hit rates from small batches.
Single-cell perturbation foundation model predicting transcriptomic responses to CRISPR and small-molecule interventions in cancer cells.
Single-sequence protein structure predictor that adapts image diffusion to generate 2D inter-residue templates, folding proteins without an MSA.
Inverse protein folding model for all-atom structures with bound ligands, nucleotides, or metal ions. Reaches 75.7% sequence recovery at metal sites.
Conditional diffusion model for 7T brain MRI denoising that turns a single 5-minute gradient-echo scan into a four-repetition-quality image.