All Competitors

Fine-tuned Enformer derivative that predicts discrete, interpretable cis-regulatory element class annotations (enhancer, promoter, insulator) directly from DNA sequence across human cell types.

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.

A 120M-parameter genomic foundation model that learns adaptive DNA token boundaries via H-Net-style dynamic chunking instead of fixed k-mer or byte-pair tokenization.

A zebrafish DNA sequence-to-function model predicting cell-type-specific single-cell expression across 85 cell-type x developmental-timepoint combinations during embryogenesis.

A 700M-parameter DNA language model pretrained on the rice pangenome, built as a reusable foundation model for crop genomics and molecular breeding.

A promptable DNA language model that generates multi-kilobase plasmid sequences from human-readable component specifications, post-trained with verifiable rewards.

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

A unified bio-language Mixture-of-Experts foundation model spanning DNA, protein sequence and structure, and biological text, applied across eight task families from a single checkpoint.

A pretrained DNA language model combining Mamba state-space layers, gated dilated convolutions, and Fourier-based attention to capture multi-scale genomic regulatory patterns.

An open autoregressive genomic foundation model (0.5B–8B params) with a 6-mer DNA tokenizer, matching Evo2-7B win rates at far higher throughput.

A 1.25B-parameter Mixture-of-Experts genomic foundation model for rice, pretrained on 422 Oryza genomes with a 1 Mbp context window.

OpenAI's first life-sciences frontier reasoning model, optimized for multi-step scientific workflows spanning protein engineering, genomics, drug-target discovery, and biochemistry reasoning.

Self-supervised generative foundation model jointly trained on 91.7M nucleotide sequences and structured annotations spanning 1.076 trillion bases, achieving SOTA on 23 nucleotide-language benchmarks.

A DNA language model that replaces fixed tokenization with conservation-guided patching, letting models up to 10x smaller match or beat state-of-the-art genomic benchmarks.

Genomic foundation model for Cypriniformes fish, built on a Mamba-2 state space model with a 32 kb context window for long-range genome modeling.

A bidirectional masked discrete diffusion language model over DNA, initialized from Nucleotide Transformer v2, that unifies DNA understanding and generation.

Family of plant genomic foundation models (0.1B-1B params) pretrained on 43 phylogenetically diverse plant genomes for regulatory, expression, and variant tasks.

A training framework that grounds genomic foundation models with a joint-embedding predictive objective, learning to predict functional representations of masked DNA rather than reconstructing tokens.

A tokenizer-free, hierarchical autoregressive genomic foundation model that adaptively chunks raw nucleotides, enabling efficient long-context learning and zero-shot variant and gene predictions.

A structure-aware generative DNA language model pretrained on influenza genomes that forecasts future antigenic variants across regions and subtypes.

An OCR-inspired vision-language model that renders DNA as visual layouts to analyze long genomic sequences with far fewer tokens than sequential tokenizers.

A family of autoregressive genomic foundation models that reconcile k-mer tokenization with single-nucleotide resolution at contexts up to 98k bp.

Retrieval-augmented genomic foundation model that adds an explicit hash-based k-mer motif memory to transformer backbones, gaining up to 14% on functional genomics tasks.