All Competitors

Multimodal protein language model that adds a continuous-token diffusion head to a discrete pLM, modeling structure without vector quantization.

Multimodal foundation model that embeds histology, transcriptomics, and clinical records in one space for patient stratification and target discovery.

Graph transformer foundation model for glycans, learning reusable embeddings of branched carbohydrate structures for glycomics prediction tasks.

Pathology foundation model that aligns whole-slide images with genomic, epigenetic, and transcriptomic data for patient-level tumor representations.

In-context learning model for cell-free RNA, meta-trained on synthetic tasks from a cfRNA structural causal model for few-shot cancer classification.

Joint embedding predictive architecture for ICU bedside waveforms, learning frozen ABP, ECG and PPG representations for five-minute risk estimation.

De novo peptide sequencing transformer that reads modified and unmodified peptides directly from tandem mass spectra without a reference database.

Tissue imaging foundation model pretrained on matched H&E histology and spatial proteomics for cross-modal inference and zero-shot retrieval.

Pan-viral genomic language model producing fixed genome-level embeddings of viral DNA and RNA, reused across classification tasks without retraining.

Designs synthesizable PROTAC degraders from reaction templates and purchasable building blocks, with reinforcement learning tuning the generator.

Prime editing efficiency prediction that quantifies per-pegRNA uncertainty, pairing a Dirichlet outcome model with conformal coverage guarantees.

ADMET property prediction for CYP inhibition, PXR activation, hERG, microsomal clearance, and permeability, all trained on public assay data.

Cross-modal translator turning routine H&E slides into virtual 21-channel multiplex immunofluorescence maps of the tumor microenvironment.

Energy-based model of protein conformational space, turning a diffusion model into a statistical potential for structure ranking and mutation scoring.

Plant genome foundation model pairing a bidirectional Mamba backbone with sparse Mixture-of-Experts, pretrained on 25.4B nucleotides from 42 species.

Structure-based drug design model that unifies de novo generation, docking, conformer generation, and pharmacophore conditioning via flow matching.

Atom-level diffusion model for de novo enzyme design that scaffolds arbitrary active-site geometries without specifying catalytic residue positions.

Prokaryotic genome language model that reads annotated replicons as ordered gene-product descriptors to predict plasmid hosts and gene essentiality.

Pan-cancer multi-omic foundation model encoding CpG-island DNA methylation and RNA-seq for zero-shot cancer classification and mutation prediction.

Conversational single-cell and spatial multi-omics brain foundation model, with zero-shot cell annotation and disease prediction across species.

Pan-cancer single-cell foundation model with a hybrid Transformer-Mamba architecture, released with the PanFoMaBench cancer evaluation benchmark.

Structure-conditioned protein sequence design, pairing a three-track architecture with discrete flow matching for fast, few-step inverse folding.

RNA inverse-folding model that generates sequences predicted to fold into a target 3D backbone, capturing non-canonical pairs and tertiary motifs.