All Competitors

RNA-binding protein predictor trained on eCLIP data that scores binding intensity along transcripts and recovers motifs via integrated gradients.

Protein-DNA binding free energy change prediction for missense mutations, with double- and single-stranded DNA binders modeled separately.

Protein language model that encodes sequences as discrete words from a learned vocabulary for zero-shot function inference and protein design.

Denoising diffusion bridge model for peptide binder design that generates ligand surfaces and backbones complementary to a target receptor surface.

Inverse protein folding model for all-atom structures with bound ligands, nucleotides, or metal ions. Reaches 75.7% sequence recovery at metal sites.

MSA design model generating alignments from protein language model embeddings to improve folding accuracy on orphan and low-homology proteins.

Whole-protein carbohydrate-binding classification from structure, for proteome-scale screening with graph convolutions over ESM2 embeddings.

Protein-ligand scoring function that conditions probabilistic geometric potentials on language model priors to rank docked poses and binding affinity.

Protein-protein binding interface prediction from conformational ensembles, resolving interfaces in flexible and intrinsically disordered regions.

Biochemistry-aware inverse folding model that augments backbone geometry with physicochemical point clouds, reaching ~90% sequence recovery on CATH.

Protein segmentation that locates folded domain, sub-domain, and disordered region boundaries from frozen ProtT5 embeddings without any training step.

Diffusion model translating in both directions between protein sequences and fluorescence microscopy images to predict subcellular localization.

Protein dynamics model that samples conformational ensembles autoregressively at slow and fast timescales, generalizing zero-shot to unseen proteins.

Protein dynamics model predicting per-residue covariances and residue-residue coupling from a static structure for fast RMSF and ensemble generation.

Protein-protein interaction predictor that adds contact-guided dual attention and a geometric encoder to frozen protein language model embeddings.

Generative de novo antibody design system jointly modeling sequence and all-atom structure, refined by scaling test-time compute.

Zero-shot peptide binder designer that runs diffusion in a pretrained protein embedding space, proposing binders without structure prediction.

RNA language model that builds base-pairing constraints into self-attention, pretrained on 20.4 million sequences for structure and function tasks.

Protein-protein interaction predictor fusing evolutionary and structural embeddings to screen bacterial and host-pathogen proteomes in minutes.

Graph deep learning framework fusing frozen protein language model embeddings with structure graphs to predict per-residue flexibility in antibodies.

Transferable coarse-grained force field for molecular dynamics of proteins, RNA, and lipids, built on the MACE equivariant graph architecture.

Structure-free virtual screening model co-embedding protein residues and small molecules from sequence and 2D chemistry, scoring a compound in 10 ms.

Protein-protein interface prediction from 3D structure using face-centered surface fingerprints and geometric graph attention, at ROC AUC 0.89.