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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1081–1104 of 2336 models
Intrinsically disordered region function prediction, scoring every residue for five binding subtypes plus disordered flexible linkers.
Cyclic peptide design conditioned on target protein structure, generating all four cyclization types via all-atom, all-bond harmonic SDE modeling.
RNA conformational ensemble generation with a diffusion model, sampling excited states and folding pathways from one structure without MSA input.
Linear B-cell epitope prediction for cancer antigens, pairing ESM-2 embeddings with an MLP classifier; ROC-AUC 0.94 on a held-out IEDB benchmark.
De novo protein binder design that recasts structure-predictor confidence as an energy function, replacing ipTM as the hallucination objective.
Protein structure retrieval model aligning 3D structures with functional text via contrastive learning, for zero-shot search of PDB and cryo-EM maps.
Compact 1.1M-parameter DNA language model distilled from Nucleotide Transformer v2, outperforming its 500M teacher on 11 of 18 benchmark tasks.
Reasoning LLM for single-cell type annotation, mapping per-cell expression to labels with marker-by-marker chains of thought on one GPU.
Agent-based pathology model that navigates whole-slide images by zooming and panning like a pathologist, scoring 88.6% on the PathMMU-HR2 benchmark.
Protein-ligand binding affinity model that tokenizes quantum electron-cloud density into discrete codes, plus a distilled cloud-free variant.
Text-to-protein design retrieving natural protein fragments as a dynamic vocabulary, matching larger baselines on under 0.04% of their training data.
Structure-constrained molecular generation using reinforcement learning over reaction templates, trained without any external property metric.
Sequence-only predictor of protein stability change on point mutation, scoring both ddG and melting temperature shift without any input structure.
Protein conformation and dynamics generation from MD data, sampling trajectories, independent ensembles, and interpolations between two known states.
RNA-binding protein predictor trained on eCLIP data that scores binding intensity along transcripts and recovers motifs via integrated gradients.
Distribution-level representation learning that embeds whole cell populations, perturbation responses, and sequence sets, not individual data points.
Protein structure prediction and peptide binder design model covering the 20 canonical amino acids plus 29 noncanonical residues.
Self-supervised transformer pretrained on millions of tandem mass spectra, giving embeddings for spectral annotation and fingerprint prediction.
Bidirectional DNA foundation model with a Mamba-attention-mixture-of-experts design, reading 1 million base pairs at single-nucleotide resolution.
EEG foundation model whose codebook tokenizer encodes Fourier phase on the unit circle, gaining six points of balanced accuracy over LaBraM.
Contrastive k-mer embedding model for sequencing reads whose latent space encodes genomic position, matching BWA-aln accuracy on ancient DNA mapping.