All Competitors

Single-cell foundation model with rank and expression-aware input streams, pairing masked gene modeling with cell-level contrastive learning.

Discrete graph diffusion model for multi-property molecular generation, composing per-property score guidance over arbitrary condition subsets.

Multimodal tokenizer for antibody CDR loops, encoding backbone dihedrals and sequence as discrete tokens that plug into antibody language models.

Cancer genomics foundation model embedding clinical gene-panel mutations into tumor subtype vectors. Pretrained on 30,328 tumors and 8 networks.

Peptide-MHC class I binding predictor that scores force-field energy terms from modeled pMHC structures, holding precision on rare HLA alleles.

Sequence-based multitask model predicting covalently ligandable cysteines and reversible ligand-binding residues across the human proteome.

Protein-ligand binding site prediction that ranks pocket residues and pocket center coordinates, staying accurate on AlphaFold-predicted structures.

Genomic foundation model that jointly encodes DNA sequence and functional omics tracks into unified single-nucleotide and interval-level embeddings.

Antibiotic resistance gene detection in metagenomes, pairing frozen ESM-1v embeddings with light classifier heads for drug class and mechanism.

RNA-small molecule binding affinity prediction from RNA sequence and compound SMILES, pairing a 56M-parameter RNA language model with ChemBERTa-2.

RNA small-molecule binding site prediction from sequence alone, pairing frozen RiNALMo embeddings with a lightweight MLP classifier.

RNA language model pretrained on 30M non-coding RNA sequences that predicts secondary structure, contacts, and splice sites without alignments.

Protein sequence-structure co-embedding model placing domains, full sequences, and short segments in one 32-dimensional contrastive space.

Structure-free peptide binder design conditioned only on a target protein sequence, using contrastive alignment to steer a latent diffusion model.

Multimodal diffusion transformer for de novo protein design, jointly generating sequence and structure conditioned on 465 Gene Ontology functions.

EHR foundation model for structured OMOP timelines that unifies patient embeddings, zero-shot outcome prediction and synthetic record generation.

De novo drug design model generating target-conditioned ligands by latent diffusion over 1D SELFIES strings, conditioned on protein sequence alone.

Cryo-EM and cryo-ET map enhancement model that sharpens density maps with a Mamba-based dual-branch UNet and local resolution-guided learning.

Binding free-energy surrogate trained on 1.4M molecular dynamics frames, ranking docking poses ~28,000x faster than physics-based MMGBSA.

Generative protein language model that designs synthetic linear epitope libraries, and classifiers that filter them by bacterial or viral origin.

Protein-ligand interaction predictor that types seven contact classes between residues and ligand functional groups from sequence and SMILES alone.

Genomic language model predicting drug-induced translational readthrough at premature stop codons, with an R2 of 0.94 across eight compounds.

Protein binder design model post-trained from a multimodal protein language model to bind proteins, peptides, small molecules, and nucleic acids.