All Competitors

Ordered-water prediction for protein structures by flow matching, adding the crystal symmetry mates that coordinate waters at lattice contacts.

Protein perturbation model conditioning substitution-effect prediction on a learned protein-level regime coordinate over 202 million sequences.

Self-supervised spatio-temporal transformer for resting-state EEG, pretrained on healthy controls and transferred to Alzheimer's screening.

Single-cell foundation model pretrained on human brain organoids that predicts transcriptome-wide responses to knockdown of any protein-coding gene.

Conditional diffusion model for 5' UTR and UTR-CDS junction design, targeting ribosome load, folding energy and codon adaptation at sampling time.

Clinical EEG foundation model reading 17 findings — seizures, spikes, slowing, burst suppression, sleep stages — from one pretrained transformer.

NMR foundation model that turns 1D 1H spectra into reusable embeddings, with a shared encoder serving denoising, peak detection, and retrieval.

Sequence-only dual-encoder contrastive model that ranks whole molecule libraries against a protein target without 3D structures or per-pair scoring.

Antifreeze protein classifier over frozen ESM-2 embeddings, trained only on sequences whose antifreeze activity was measured in the lab.

Single-sequence protein structure prediction on a compact distance-guided folding trunk, with retrieval-augmented refinement from PDB templates.

EEG foundation model pairing masked contextual modelling with cross-view invariance learning over 11,000 hours of routine clinical recordings.

Protein language model that supervises embedding geometry with inter-residue contacts, so representation distance tracks physical distance.

Protein language model embedding enrichment that injects structural and dynamical signal as a low-energy residual, using sequence alone at inference.

Longitudinal multimodal patient foundation model for oncology, fusing clinical records, DNA, RNA, and H&E pathology into one patient-state embedding.

3D brain MRI synthesis from separate hemisphere and cerebellum-brainstem diffusion priors, assembled and refined into one volume with editable parts.

Long-context RNA foundation model reading whole mRNA transcripts at single-nucleotide resolution, pretrained at a native 10,240 nt context.

Codon language model for mRNA prediction and coding sequence design, steering synonymous codon choice with a swappable host usage prior at inference.

Single-cell perturbation prediction model that splits a transcriptional response into systematic, perturbation-specific, and population-level parts.

Molecular foundation model for drug discovery that masks entire data modalities and predicts their latent embeddings from the remaining ones.

Designs macrocyclic peptide molecular glues bridging two target proteins from sequence alone, validated as VHL-recruiting degraders in cells.

EEG foundation model trained jointly across nine heterogeneous datasets, decoding by matching brain signals to language-model embeddings of labels.

State-space estimator that infers fine-scale recombination maps from population genotypes, reusing one frozen checkpoint across species.

Genomic foundation model that pairs 650 kb of gene-centered DNA with transcription factor activity to predict expression in unseen cell types.