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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 937–960 of 2336 models
Protein multi-conformation predictor that scores per-residue flexibility, then masks MSA columns to steer AlphaFold 2 toward alternative states.
Conditional chemical language model prompted with a protein target and mechanism of action to score and design molecules without structural input.
Protein-DNA binding free energy change prediction for missense mutations, with double- and single-stranded DNA binders modeled separately.
Protein foundation model built on Bayesian Flow Networks, prompted with MSA profiles for structure- and function-preserving sequence design.
Fungal genome mining framework that detects biosynthetic gene clusters and identifies their core enzymes from a pretrained Pfam-domain transformer.
Antibody, nanobody, and T-cell receptor structure prediction that resolves bound and unbound conformations separately in under a second per domain.
Cross-species brain spatial transcriptomics foundation model pretrained on 133M cells from human, macaque, marmoset, and mouse whole brains.
Site-specific structure prediction conditioning AlphaFold3 diffusion on a fixed receptor and known binding pocket. 81.2% success on PoseBusters V2.
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.
Open medical multimodal models from Google, built on Gemma 3 with a medically tuned SigLIP vision encoder for clinical text and image understanding.
Spatial proteomics imputation model inferring surface protein abundance from transcriptomics-only tissue sections via dual graph attention networks.
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.
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.
Multimodal single-cell foundation model pretrained on 4M+ co-assayed cells, predicting 382 surface proteins from transcriptomes alone, zero-shot.
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.
De novo protein backbone generator trained on low-confidence AlphaFold structures as corrupted data, reaching 86% designability at 700 residues.