All Competitors

Specificity Foundation Model that predicts transcription factor-DNA binding specificity from sequence using a physics-derived dual-encoder with symmetric contrastive learning.

Specificity Foundation Model that predicts CRISPR gRNA off-target DNA specificity from sequence using a physics-derived dual-encoder with symmetric contrastive learning.

Specificity Foundation Model that predicts small-molecule drug-target protein specificity from sequence using a physics-derived dual-encoder with symmetric contrastive learning.

Specificity Foundation Model that predicts enzyme-substrate specificity from sequence using a physics-derived dual-encoder with symmetric contrastive learning.

Specificity Foundation Model that predicts peptide-MHC binding specificity from sequence using a physics-derived dual-encoder with symmetric contrastive learning.

Specificity Foundation Model that predicts microRNA-mRNA target specificity from sequence using a physics-derived dual-encoder with symmetric contrastive learning.

Self-supervised foundation model that learns reusable representations of cancer genomes from somatic SNVs and copy-number alterations across 33 tumor types.

Genetically aligned foundation model for blood smear cytology that links single white-blood-cell morphology to chromosomal aberrations and mutations for AML/APL diagnosis.

Contrastive promoter-protein pretraining that aligns bacterial promoters with their encoded proteins to learn regulatory genomics representations.

A self-supervised metabolomic foundation model pretrained on NMR metabolite profiles from 430,000+ UK Biobank participants, applied without backbone retraining to aging, subtyping, and risk tasks.

A multi-organ foundation model aligning histology image features with spatial-transcriptomics gene expression across 13 organs for zero-shot virtual ST and survival prediction.

A virtual cell foundation model pretrained on 23M+ cells from 5,000 patient samples for drug target and biomarker discovery.

Contrastively fine-tuned ESM-2 (35M and 150M) protein language models that produce general-purpose sequence embeddings where biological similarity maps to embedding proximity.

A protein language model that aligns ESM sequence embeddings with molecular-dynamics trajectory embeddings via contrastive learning for zero-shot mutation-effect prediction.

A foundation model that predicts spatial transcriptomics and proteomics directly from routine H&E whole-slide images using a vision transformer aligned with a language model.

A CLIP-style dual-encoder model that learns a shared peptide-spectrum representation for zero-shot peptide-spectrum-match inference in DIA proteomics.

Generates single-cell transcriptomic profiles from structured biological metadata via contrastive language-omics pretraining and a diffusion transformer.

Dual-encoder contrastive model that embeds protein domains and peptides into a shared space to predict domain-peptide binding specificity at proteome scale.

Multimodal molecular foundation model fusing SELFIES sequences, 2D graphs, text descriptions, and knowledge-graph embeddings via contrastive pretraining for property prediction.

Dual-encoder contrastive model that retrieves enzymes for query reactions by matching reaction fingerprints to protein sequence embeddings.

Generalist EEG-to-text foundation model that translates EEG segments into clinically grounded natural-language descriptions via spectro-spatial grounding and state-space reasoning.

A contrastive cross-modal encoder that aligns protein sequence (ESM-2) and structure (ProteinMPNN) representations into a shared embedding space for cross-modal retrieval.

Contrastive model built on a genomic language model that predicts physical protein-protein interactions across a microbial proteome in linear time.

Contrastive antibody language model that predicts antibody-antigen binding specificity directly from amino acid sequence using a dual-encoder, cross-attentive architecture.