All Competitors

A hierarchical three-stage cascade that generates chemically modified and macrocyclic peptides in HELM notation, supporting de novo design and constrained infilling.

Multimodal deep-learning framework that detects and localizes DNA lesions directly from native nanopore sequencing, built on the damage-aware LesionBERT foundation model.

A 110M-parameter multimodal RNA language model that designs RNA sequences from secondary structure, consensus, and Gene Ontology constraints via discrete diffusion.

SMILES-based chemical language models pretrained on 100M+ molecules to natively represent therapeutic peptide chemistry, including non-canonical residues.

BERT-style language model that learns contextual representations of somatic mutations from clinical sequencing of >210,000 patients for cancer subtyping and therapy response.

An mRNA language foundation model trained on ~115M coding sequences across the tree of life for unified mRNA sequence perception and generation.

A pipeline that builds whole-metagenome embeddings directly from raw DNA reads using genomic language models and FAISS k-means clustering, without taxonomic or functional annotation.

Pan-cancer multi-omic BERT-like foundation model that jointly encodes CpG-island DNA methylation and RNA-seq for zero-shot cancer classification and mutation prediction.

A fully open structure-guided RNA foundation model pretrained on ~21M RNA sequences paired with secondary structures, enabling robust structural and functional inference.

A BERT-style transformer language model built on the Geneformer framework and trained on zebrafish single-cell transcriptomics to produce gene and cell embeddings for developmental analysis.

A 52.3M-parameter bimodal masked language model that jointly learns representations of bulk RNA-seq expression and DNA methylation for cancer genomics.

A family of codon-resolution language models trained on 130 million coding sequences from 20,000 species, revealing context-dependent codon grammar governing translation and mRNA stability.

A 7-billion-parameter encoder-only DNA foundation model trained on 10.6 billion nucleotides from 796 species for functional genomics and synthetic biology.

A 1.6-billion-parameter RNA foundation model pretrained on 42 million non-coding RNA sequences, achieving state-of-the-art performance across 24 of 26 RNA understanding benchmarks.

Single-cell RNA-seq foundation model series pretrained on 50 million human cells with full transcriptome-scale context for cell clustering and perturbation modeling.

BERT-based model pretrained on 15-state ROADMAP chromatin annotations across 127 human cell types to uncover chromatin-state motifs and predict gene expression.

Vision-language foundation model pre-trained on screening mammogram-report pairs to improve data efficiency and robustness in breast cancer detection.

Codon-level BERT model that captures genomic signals invisible to amino acid models, outperforming billion-parameter PLMs with just 86M parameters.

Large-scale chest X-ray vision-language pretraining model that learns image-report alignment for zero-shot and few-shot radiograph classification.

DNA language model trained on over 800 species spanning 500 million years of evolution to capture conserved regulatory elements and their evolution beyond sequence alignment.

A BERT-based RNA language model pre-trained on 2M+ pre-mRNA sequences from 72 vertebrate species for splicing prediction and variant effect analysis.

Multi-modal self-supervised model pre-trained on regulatory genome sequences and transcription factor binding matrices for cell-type-specific regulatory prediction.