All Competitors

Gradient-free protein design framework that treats engineering as Monte Carlo sampling of a user-defined energy landscape over pretrained models.

De novo protein binder design that conditions AlphaFold2-Multimer hallucination on a chosen fold, from TIM barrels to VHH nanobody scaffolds.

Multimodal all-atom generative model for zero-shot de novo antibody and protein-binder design, validated by wet-lab hit rates from small batches.

Single-cell perturbation foundation model predicting transcriptomic responses to CRISPR and small-molecule interventions in cancer cells.

Conditional diffusion model for 7T brain MRI denoising that turns a single 5-minute gradient-echo scan into a four-repetition-quality image.

RNA-protein complex refinement via diffusion, repositioning the protein against the RNA to improve AlphaFold 3 and ProRNA3D-single backbones.

Inverse protein folding model for all-atom structures with bound ligands, nucleotides, or metal ions. Reaches 75.7% sequence recovery at metal sites.

Protein-protein interaction predictor that adds contact-guided dual attention and a geometric encoder to frozen protein language model embeddings.

Tissue-specific RNA splicing prediction from pre-mRNA sequence, scoring how variants shift splice-site usage across 18 human tissues.

Allosteric binding site prediction from protein sequence alone, using LoRA-tuned protein language models conditioned on the orthosteric pocket.

Automated cryo-EM structure determination that fuses density maps with AlphaFold3 predictions, averaging a TM-score of 0.93 on high-resolution maps.

All-atom biomolecular structure prediction with adapters for allosteric states, user-defined interface constraints, and binding affinity.

Single-cell perturbation-response model that predicts transcriptomic and imaging outcomes of unseen genetic perturbations via a VAE with attention.

Family of CNN foundation models pretrained on multimodal radiology images, a domain-specific alternative to ImageNet transfer learning weights.

Multimodal foundation model for cardiac biosignals, pretrained by masked modeling on ECG, PPG, and clinical text from ~1.7 million individuals.

Biomedical vision-language assistant for medical visual question answering, pairing Phi-2 with a vision encoder in a 4.2B-parameter model.

Backmapping model that rebuilds all-atom protein and nucleic acid structures from coarse-grained beads and inpaints unresolved residues.

Self-supervised medical imaging foundation model pretrained on 3.3 million CT, X-ray, ultrasound, pathology, OCT, fundus, and dermoscopy images.

Proteomics foundation model for peptide-spectrum scoring and open de novo sequencing, reading over 1,300 modifications from tandem mass spectra.

Histopathology image synthesis from a latent diffusion model conditioned jointly on unpaired diagnostic text reports and cell-type masks.

Apple's foundation model trained on behavioral signals from wearables, modeling 27 HealthKit metrics to improve predictions across 57 health tasks.

EEG foundation model with cross-scale spatiotemporal tokenization and sparse structured attention, evaluated on 11 decoding tasks across 16 datasets.

De novo protein backbone design conditioned on a target per-residue flexibility profile, with SE(3)-equivariant flow matching and MD validation.