All Competitors

Structure-based 3D molecule generation that denoises a ligand at atom level and motif level at once, conditioned on the target protein's pocket.

Enzyme redesign framework built on a structure-to-sequence protein network, scoring mutants and generating sequences without retraining.

Diffusion protein language model for de novo design conditioned jointly on GO terms, InterPro domains, EC numbers, motifs, and backbone structure.

De novo VHH nanobody binder design that conditions AlphaFold-Multimer with structural templates and language model sequence priors, no retraining.

Generative foundation model that co-generates sequence and 3D coordinates for proteins, small molecules, and crystals under functional objectives.

Structure-based molecule optimization that steers a Bayesian flow network with property gradients over 3D coordinates and atom types at once.

Atom-level diffusion model for de novo enzyme design that scaffolds arbitrary active-site geometries without specifying catalytic residue positions.

Structure-conditioned fine-tune of ESM2 for protein mutation-effect prediction, matching ESM3-level accuracy after roughly an hour of fine-tuning.

All-atom protein design diffusion model conditioned on ligands, nucleic acids, and other non-protein atoms, supporting enzyme and DNA binder design.

Protein-ligand foundation model that maps coarse-grained structural representations directly to binding affinity, running ~26x faster than Boltz-2.

Ab initio RNA 3D structure prediction from a single sequence, using a composite-likelihood language model and a denoising end-to-end structure module.

Nanomaterial-protein interaction prediction from protein sequence, structure, and experimental context that generalizes to unseen materials.

Pharmacophore-conditioned diffusion model generating 3D molecular graphs that satisfy a given pharmacophore hypothesis without a target structure.

Protein dynamics model predicting per-residue probability of microsecond-millisecond conformational exchange from sequence or structure.

Pretrained antibody structure predictor that outputs full paired heavy/light 3D structures faster than protein language models generate embeddings.

Structure-based drug design that generates 3D ligands for a protein pocket entirely in the continuous parameter space of a Bayesian flow network.

Protein foundation model for de novo enzyme design that co-designs sequence and 3D structure under small-molecule ligand guidance, at 730M parameters.

Structure-to-sequence protein design network inverting trRosetta, predicting coevolution features from a backbone to generate stable sequences.

Structure-based drug design model generating 3D ligands in protein pockets under gradient guidance for affinity, synthesizability, and selectivity.

Diffusion-based generative RNA model for de novo sequence design, conditioned on function, RNA family, structure, or binding proteins.

Single-nucleotide-resolution RNA foundation model pretrained on non-coding RNAs with ELECTRA-style replaced-token detection for regulatory inference.

Open-source PyTorch reproduction of AlphaFold 3 under Apache 2.0, matching or exceeding AF3 on protein-ligand, protein-protein, and RNA benchmarks.

Protein model accuracy estimation from MSA co-evolution and homologous templates, predicting per-residue lDDT with a triangular-attention backbone.