Audience
Computational biologists and genetic engineers in need of a tool to model and design biological sequences across multiple modalities, enhancing research in genomics and synthetic biology
About Evo 2
Evo 2 is a genomic foundation model capable of generalist prediction and design tasks across DNA, RNA, and proteins. It utilizes a frontier deep learning architecture to model biological sequences at single-nucleotide resolution, achieving near-linear scaling of compute and memory relative to context length. Trained with 40 billion parameters and a 1 megabase context length, Evo 2 processes over 9 trillion nucleotides from diverse eukaryotic and prokaryotic genomes. This extensive training enables Evo 2 to perform zero-shot function prediction across multiple biological modalities, including DNA, RNA, and proteins, and to generate novel sequences with plausible genomic architecture. The model's capabilities have been demonstrated in tasks such as designing functional CRISPR systems and predicting disease-causing mutations in human genes. Evo 2 is publicly accessible via Arc's GitHub repository and is integrated into the NVIDIA BioNeMo framework.
Other Popular Alternatives & Related Software
GPT-Rosalind
GPT-Rosalind is a purpose-built frontier reasoning model developed by OpenAI to accelerate scientific research across biology, drug discovery, and translational medicine. It is designed specifically for life sciences workflows, where researchers must navigate large volumes of literature, experimental data, and specialized databases to generate and validate new ideas. It combines deep domain understanding in areas such as chemistry, genomics, protein engineering, and disease biology with advanced tool-use capabilities, allowing it to interact with scientific databases, analyze experimental outputs, and support complex, multi-step reasoning tasks. It can assist with evidence synthesis, hypothesis generation, literature review, sequence interpretation, and experimental planning, helping scientists move faster from raw data to actionable insights. GPT-Rosalind transforms complex, time-intensive research processes into more efficient AI-assisted workflows.
Learn more about GPT-Rosalind
AlphaGenome
AlphaGenome is a Google DeepMind API and Python toolkit for computational genomics research. Researchers submit DNA sequences or genetic variants to obtain model predictions about gene expression, RNA splicing, chromatin accessibility, transcription-factor binding, histone marks, and genome contacts. The API supports variant scoring and analyses of genomic regions in human and mouse data, with tutorials and reference materials for programmatic workflows. It can help researchers investigate how genetic changes may affect gene regulation and prioritize hypotheses for further study. Access is provided through a hosted API, with a Python package for local client-side use and notebook examples. AlphaGenome is intended for research and is not validated or approved for clinical or diagnostic use; its predictions should not be treated as clinical results.
Learn more about AlphaGenome
NVIDIA BioNeMo
BioNeMo is an AI-powered drug discovery cloud service and framework built on NVIDIA NeMo Megatron for training and deploying large biomolecular transformer AI models at a supercomputing scale. The service includes pre-trained large language models (LLMs) and native support for common file formats for proteins, DNA, RNA, and chemistry, providing data loaders for SMILES for molecular structures and FASTA for amino acid and nucleotide sequences. The BioNeMo framework will also be available for download for running on your own infrastructure. ESM-1, based on Meta AI’s state-of-the-art ESM-1b, and ProtT5 are transformer-based protein language models that can be used to generate learned embeddings for tasks like protein structure and property prediction. OpenFold, a deep learning model for 3D structure prediction of novel protein sequences, will be available in BioNeMo service.
Learn more about NVIDIA BioNeMo
Evo Designer
Evo Designer is an advanced tool developed by the Arc Institute, leveraging the capabilities of the Evo 2 genomic foundation model to facilitate DNA sequence generation and analysis. This platform enables users to input nucleotide sequences or specify organisms, prompting the model to generate corresponding DNA sequences. It provides comprehensive annotations of coding regions and, for prokaryotic sequences, offers 3D protein visualizations utilizing ESMFold. Additionally, Evo Designer evaluates sequences by scoring their perplexity and per-nucleotide entropy, assisting researchers in assessing sequence complexity and variability. The underlying Evo 2 model is trained on over 9 trillion nucleotides from a diverse array of prokaryotic and eukaryotic genomes, employing a deep learning architecture that models biological sequences at single-nucleotide resolution with a context window extending up to 1 million tokens.
Learn more about Evo Designer
Integrations
Company Information
Arc Institute
United States
arcinstitute.org/tools/evo
Videos and Screen Captures
Other Useful Business Software
$300 Free Credits to Build on Google Cloud
Put your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
Product Details
Platforms Supported
Cloud
Training
Documentation
In Person
Support
Online
Evo 2 Frequently Asked Questions
Q: What kinds of users and organization types does Evo 2 work with?
Evo 2 works with these users and organization types: Mid Size Business, Small Business, Enterprise, Freelance, Nonprofit, Government, and Startup.
Q: What languages does Evo 2 support in their product?
Evo 2 supports these languages: English.
Q: What kind of support options does Evo 2 offer?
Evo 2 offers support via online.
Q: What other applications or services does Evo 2 integrate with?
Evo 2 integrates with: NVIDIA BioNeMo, Evo Designer, GitHub, and Hugging Face.
Q: What type of training does Evo 2 provide?
Evo 2 provides training in the form of documentation and in person.
