All Competitors

Structure-based conformational B-cell epitope predictor that scores local antigen surface patches with ESM-2 embeddings and an ensemble MLP.

Foundation model for tandem mass spectrometry that embeds MS/MS spectra into a learned chemical space, resolving isomers and classifying disease.

Enzyme backbone design model that adds substrate and catalytic-site control to a pretrained SE(3) flow-matching generator via a lightweight adapter.

Photoplethysmography foundation model whose hierarchical convolutional encoder exposes one embedding per temporal scale and runs entirely on-watch.

Latent diffusion model that backmaps coarse-grained protein structures to all-atom detail, with bond geometry learned into the latent space.

Open-source Apache-2.0 reproduction of AlphaFold3 that predicts all-atom structures of proteins, RNA, DNA, small molecules, and their complexes.

Protein-ligand cofolding model that predicts 3D complex structures with SO(3)-equivariant diffusion, trained on physics-based synthetic data.

Nuclei detection and classification in histopathology images, using one specialised decoder per cell type over a shared convolutional encoder.

Synthetic 3D MRI generation spanning brain, prostate, breast and abdomen, with the T1, T2 or FLAIR contrast selected at sampling time.

Siamese protein language model whose embedding distances approximate TM-score and lDDT, enabling alignment-free protein structure comparison.

EEG foundation model whose learned queries map any electrode montage into a fixed latent space, scaling linearly in the number of channels.

Multimodal EHR foundation model that fuses polygenic risk scores into a GPT-2-style backbone by cross-attention for zero-shot disease risk prediction.

Vision Transformer foundation model for spatial metabolomics, pretrained on ~4,000 curated METASPACE mass spectrometry imaging datasets.

Clinical laboratory morphology vision-language model that identifies cells, describes them and localizes them in blood, urine and stool microscopy.

Perturbation-trained single-cell foundation models (up to 3B parameters) that jointly model genes, cells, and compounds for precision oncology tasks.

Multimodal LLM that tokenizes single cells into discrete VQ-VAE codebook tokens, letting one model reason jointly over transcriptomes and text.

Multimodal molecular large language model grounding molecule understanding and generation in fine-grained, multi-level chemical knowledge.

Sequence-based binding site predictor spanning protein-DNA, protein-RNA, protein-protein, and antibody-antigen interfaces via a fine-tuned ProtT5.

fMRI foundation model of the human brain connectome: 1.2B parameters and brain-environment interaction tokens for behavior and disease prediction.

Self-supervised foundation model for human cortical cytoarchitecture, encoding histological brain sections into anatomically meaningful features.

Transcription factor binding site prediction model that refines a DNABERT-2 backbone with contrastive learning across diverse TFBS types.

Structure prediction backbone that swaps AlphaFold3-style triangle attention for triangle multiplication, cutting compute without losing accuracy.

Vision-language encoders for chest CT that align 3D volumes with radiology reports using contrastive, report-generation, and masked-image objectives.