All Competitors

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

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

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.

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

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

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.

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.

Transcriptome-guided diffusion model generating Cell Painting images for unseen perturbations, improving MOA retrieval accuracy by 16.9% over IMPA.

Image-to-image translation from label-free phase-contrast microscopy to H&E-like images, so pretrained histopathology models run on live cells.

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

Structure-based drug design model generating 3D ligands in a protein pocket with interaction-guided flow matching and a learned atom-count predictor.

Modality-agnostic foundation model for human brain imaging that runs five core neuroimaging tasks across uncalibrated CT and MRI without retraining.

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

EEG foundation model pretrained by latent diffusion instead of masked reconstruction, jointly optimizing a ViT encoder with a DiT denoiser.

Histopathology classifier separating atypical from normal mitotic figures, LoRA-adapting a DINOv3 vision transformer with 1.3M trainable parameters.

Protein degrader design framework that mines fragment-target data to build PROTACs and predicts degradation potency (DC50) and maximal degradation.

Generative antibody model that produces light-chain sequences conditioned on a heavy chain, pairing a RoBERTa encoder with a GPT-2 decoder.