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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 577–600 of 2565 models
Phosphopeptide detectability prediction for mass spectrometry, rescoring DDA identifications and pruning DIA spectral libraries to cut search time.
Paired-sequence protein language model that jointly encodes two interacting chains to predict interactions, binding affinity, and interface contacts.
Diffusion generative model for structure-based peptide inverse folding, pairing a geometric GNN encoder with a Transformer denoiser.
Genomic foundation model for Cypriniformes fish, built on a Mamba-2 state space model with a 32 kb context window for long-range genome modeling.
Post-hoc method that restores monotonic scaling to ESM-2 embeddings, yielding Matryoshka-style nested representations for variant effect prediction.
EEG-to-text foundation model that turns raw recordings into clinically grounded natural-language narratives instead of fixed-label classifications.
Bacterial proteome foundation model that learns contextualized gene and whole-genome representations from tens of thousands of complete genomes.
Protein inverse folding model aligning ProteinMPNN by multi-objective preference optimization to improve developability without losing fold fidelity.
Neural Hamiltonian flow for protein sequence generation with inference-time control over composition and net charge via analytical bias potentials.
Self-supervised transformer for population genetics, pretrained on 1000 Genomes data, that detects positive selection via haplotype-wise attention.
Cross-modal protein encoder that aligns ESM-2 sequence embeddings with ProteinMPNN structure embeddings in a shared space for cross-modal retrieval.
Dual-target structure-based drug design that jointly diffuses a ligand graph and two pocket-specific binding poses in one end-to-end pass.
Generative single-cell foundation model trained on 100M+ transcriptomes that predicts how genetic perturbations reshape cell trajectories over time.
Renal pathology foundation model self-supervised on a million kidney biopsy tiles spanning glomeruli, interstitium, and surrounding structures.
Small-molecule drug discovery foundation model covering ADMET, retrosynthesis, drug-target activity, and molecular optimization in a 2.6B checkpoint.
Renal pathology foundation model whose learning unit is a whole detected glomerulus, pretrained on over a million of them across four biopsy stains.
DNA foundation model using masked discrete diffusion to unify bidirectional sequence understanding and de novo generation in one architecture.
Virtual staining from H&E histology to immunohistochemistry, using a Schrödinger bridge trained on unpaired slides with a topology-preserving term.
ECG foundation model pretrained with a Split-Mask-Join masking objective and spatial reduction attention, aimed at diagnosis where labels are scarce.
Self-supervised SE(3) geometric pretraining for protein backbone generators, improving designability, motif scaffolding, and conformational ensembles.
Graph-transformer foundation model pretrained on 3M protein pockets and 5M molecules as E(3)-equivariant graphs for protein-ligand representation.
Multimodal foundation model pretrained on ~500K unlabeled PubChem molecules that jointly predicts nine thermophysical properties of small molecules.
Large language model trained on functional genomics data to prioritize novel therapeutic targets from genome-wide CRISPR knockout screens.