All Competitors

Rigid protein-protein docking model that predicts a complex from two unbound structures in one pass, with no candidate sampling or refinement.

BERT adaptation for structured OMOP electronic health records that inserts artificial time tokens between visits so intervals shape the embeddings.

Multi-modal self-supervised transformer for regulatory genomics, pre-trained on DNA sequence together with transcription factor binding matrices.

Protein complex structure prediction model extending AlphaFold 2 with paired MSA processing and ipTM scoring for multi-chain, multimeric assemblies.

Transformer that predicts gene expression and epigenomic signals from 200kb of DNA sequence, capturing distal enhancers up to 100kb from a promoter.

Protein model quality assessment predicting per-residue lDDT from a single structure, using ultrafast shape recognition to encode residue topology.

Histopathology patch encoder self-supervised with BYOL on 2.7M unlabeled H&E tiles, using attention that gates feature channels with pooled context.

Residue-level binding site prediction from a bare protein sequence, ensembling six neural nets over protein, DNA/RNA and small-molecule interfaces.

Metabolite-likeness scoring ranks any chemical structure by its distance from a learned hypersphere of known endogenous metabolites.

Suite of six protein language models, including ProtBERT and ProtT5, trained on up to 393 billion amino acids without multiple sequence alignments.

Per-residue AlphaFold2 pLDDT confidence regressed from sequence by a bidirectional LSTM, with no structure prediction and no database lookup.

Chemical language model for small-molecule drug discovery, embedding SMILES for property prediction and sampling new molecules from its latent space.

Protein structure prediction model that folds amino acid sequences into 3D structures with atomic accuracy, scoring a median GDT of 92.4 at CASP14.

Protein structure and complex prediction from sequence, in a three-track network that reasons over alignments, distances, and 3D coordinates at once.

Protein language model for zero-shot variant effect prediction, scoring mutations by log-odds from evolutionary sequences with no MSA or assay data.

Drug repurposing model that predicts a compound's L1000 transcriptional signature from SMILES and ranks it against a disease gene signature.

Sleep staging model that segments polysomnography from any single EEG and EOG channel pair, labelling stages at resolutions finer than 30 s epochs.

Single-cell perturbation prediction model that forecasts transcriptional responses to drug combinations and doses never experimentally measured.

Transformer protein language model trained on 250 million protein sequences that learns structural and functional representations without supervision.

Self-supervised molecular graph model contrastively pretrained on ~10M unlabeled PubChem molecules, then fine-tuned for property prediction.

Bidirectional transformer for DNA using k-mer tokenization, fine-tunable for promoter, splice site, and transcription factor binding prediction.

EEG foundation model pretrained on clinical recordings with a wav2vec 2.0-style contrastive task, transferring to BCI decoding and sleep staging.

Histopathology patch encoder giving 1024-dimensional embeddings for slide search and cancer subtyping, trained on TCGA with weak slide labels.