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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 1–15 of 15 filtered models
DNA-binding residue prediction across folded domains and disordered protein regions, with contrastive training that suppresses cross-predictions.
Encoder-decoder Transformer that generates intrinsically disordered protein sequences conditioned on target conformational-ensemble descriptors.
Sequence-to-ensemble predictor that generates conformational ensembles of intrinsically disordered proteins zero-shot, with no per-sequence refitting.
Evolution-guided diffusion model that generates temporal protein folding pathways, from unfolded chain to native state, rather than static structures.
Flow-matching model that predicts protein conformational ensembles across the order-disorder continuum, from folded domains to disordered chains.
Intrinsically disordered region function prediction, scoring every residue for five binding subtypes plus disordered flexible linkers.
Protein segmentation that locates folded domain, sub-domain, and disordered region boundaries from frozen ProtT5 embeddings without any training step.
Protein-protein binding interface prediction from conformational ensembles, resolving interfaces in flexible and intrinsically disordered regions.
Contact map and interface residue prediction for intrinsically disordered regions from sequence, outperforming AlphaFold-Multimer and AlphaFold3.
Intrinsic disorder prediction from protein sequence at proteome scale, distilling consensus disorder scores and AlphaFold2 pLDDT into one network.
Phase separation prediction from sequence alone, pairing a protein language model with MD-trained conformational features to score every residue.
Predicts the radius of gyration of intrinsically disordered proteins from 23 physics-derived sequence features, screening missense mutants in bulk.
Protein language model that annotates intrinsically disordered regions per residue from sequence alone, without MSAs or biophysical features.
Per-residue AlphaFold2 pLDDT confidence regressed from sequence by a bidirectional LSTM, with no structure prediction and no database lookup.