All Competitors

Echocardiography vision foundation model self-distilled on 20 million ultrasound images from 11 clinical centres, with swappable task decoders.

EEG foundation model that makes each electrode its own token stream, pretrained by causal next-signal prediction over 138 electrode positions.

Joint embedding space for common SNPs and free-text clinical concepts, aligned by contrastive learning over GWAS, biobank and knowledge-graph pairs.

Per-residue classifiers fine-tuned from ESM-2 and ProtT5 that label 20 UniProt protein features and read out what a missense variant disrupts.

CT segmentation foundation model that uses task prompts to segment 83 anatomical structures and lesions across whole-body scans in a single network.

Codon-resolution language model suite pairing a bidirectional encoder with an autoregressive decoder over protein-coding sequences.

Chemical language model that translates IR, UV-Vis and 1H NMR spectra into SMILES structures, replacing the enumerate-and-filter CASE workflow.

RNA foundation language model pretrained on mammalian and viral genomes, fine-tuned to predict translation efficiency, half-life, and splice sites.

Mamba-based mature RNA foundation model, contrastively trained on splice isoforms and 400+ mammalian species orthologs for mRNA property prediction.

Multimodal protein model pairing a sequence encoder with a Gene Ontology branch, trained in recursive cycles that clean their own noisy labels.

Predicts binding free energy change (ΔΔG) at protein-protein interfaces by scoring bound and unbound states with an inverse folding model.

All-atom protein dynamics simulator that bridges consecutive MD snapshots directly using SO(3)-equivariant two-sided stochastic interpolants.

Protein-ligand binding affinity prediction for Kd, Ki and IC50 from a pocket structure and a SMILES string, with no docked complex required.

Zero-shot mutation effect scoring for designed and viral proteins, mapping frozen ESM2 representations onto MD and normal-mode dynamic properties.

AAV capsid design platform for gene therapy that steers a peptide language model toward inserts combining receptor targeting and production fitness.

Masked-autoencoder foundation model for chest radiographs, self-supervised on 1.04 million unlabelled images for disease screening and localization.

Protein language model trained from scratch on MD and normal-mode dynamics, representing residue fluctuation and co-movement from sequence.

Self-supervised 12-lead ECG encoder that predicts masked patches in latent space, using a cross-lead attention mask shaped by clinical reading.

Biomolecular structure prediction foundation model covering proteins, small molecules, DNA, RNA, and glycans in a single diffusion framework.

Generative foundation model for cryo-EM density maps using flow matching, enabling zero-shot denoising, map sharpening, and missing wedge restoration.

Masked-autoencoder foundation model pretrained on digital-stethoscope heart sounds and single-lead ECG for cardiovascular disease detection.

Multimodal LLM aligning natural language, small molecules and proteins in any direction, turning prose design goals into molecules or enzymes.

Protein fitness prediction model meta-trained across ProteinGym deep mutational scans, transferring to unseen assays in context with no task data.