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Every biological foundation model, evaluated and ranked by the bio.rodeo team
Showing 193–216 of 1004 filtered models
Diffusion generative model for structure-based peptide inverse folding, pairing a geometric GNN encoder with a Transformer denoiser.
Post-hoc method that restores monotonic scaling to ESM-2 embeddings, yielding Matryoshka-style nested representations for variant effect prediction.
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.
Cross-modal protein encoder that aligns ESM-2 sequence embeddings with ProteinMPNN structure embeddings in a shared space for cross-modal retrieval.
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.
Contrastive antibody language model predicting antibody-antigen binding specificity from sequence with a dual-encoder, cross-attentive architecture.
Polarizable machine-learning interatomic potential extending MACE with long-range electrostatics, trained on 100M OMol25 DFT calculations.
Enzyme function prediction model that uses contrastive learning to assign the first three EC digits to enzymes with functions unseen during training.
Protein-ligand binding affinity scorer using an SE(3)-equivariant graph network trained on 741,706 co-folded complexes with target-disjoint splits.
Conditional variational autoencoder for antimicrobial peptide design that disentangles sequence, function, and length for independent control.
Protein evolution model that learns indel dynamics and epistasis from unaligned sequences, simulating trajectories that yield functional proteins.
Retrieval-augmented framework for de novo peptide binder design that conditions generation on retrieved, structurally aligned binding evidence.
Diffusion model that generates continuous-time, all-atom biomolecular trajectories, reproducing conformational kinetics far more cheaply than MD.
Protein language model that encodes sequences as discrete words from a learned vocabulary for zero-shot function inference and protein design.
Latent diffusion model that designs D-peptide binders against native L-protein targets, generalizing across chirality via axial vector features.
Universal all-atom machine-learning force field for molecular dynamics, with ab initio-level accuracy on solvated biomolecules of ~1,500 atoms.
Protein-ligand foundation model that maps coarse-grained structural representations directly to binding affinity, running ~26x faster than Boltz-2.
Multimodal generative model predicting viral antigenic change zero-shot from disentangled evolutionary, physicochemical, and structural signals.
Unified drug design engine for protein-ligand structure prediction, binding affinity estimation, and compound generation from Isomorphic Labs.