Understanding the Language of Life’s Biomolecules Across Evolution at a New Scale with Evo 2
AI has evolved
from an experimental curiosity to a driving force within biological research. The convergence of deep learning algorithms, massive omics datasets, and automated laboratory workflows has allowed scientists to tackle problems once thought intractable—from rapid protein structure prediction to generative drug design, increasing the need for
AI literacy among scientists
. With this momentum, we find ourselves on the cusp of the next paradigm shift: the emergence of powerful AI foundation models purpose-built for biology.
These new models promise to unify disparate data sources—genomic sequences, RNA and proteomic profiles, and, in some cases, scientific literature—into a single, coherent understanding of life at the molecular, cellular, and systems levels. Learning biology’s language and structure opens doors to transformative applications, such as smarter drug discovery, rational enzyme design, and disease mechanism elucidation.
As we set the stage for this next wave of AI-driven breakthroughs, it is clear that these foundation models will not merely accelerate progress; they stand poised to redefine what is possible in biological research.
Test
Evo 2 as an NVIDIA BioNeMo NIM microservice
for free with the
/NVIDIA/bionemo-examples
example notebook.
Explore a complete
reference workflow for protein design
.
Start training Evo 2 on your data today in
BioNeMo Framework
.
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latest NVIDIA BioNeMo platform updates
.
A leap forward in sequence modeling and design from molecular to genome-scale
The
first Evo model
from November 2024 represented a groundbreaking milestone in genomic research, introducing a foundation model capable of analyzing and generating biological sequences across DNA, RNA, and proteins.
Published at a time when most models were restricted to single modalities or short contexts, Evo is known for its ability to operate across scales—ranging from molecular to genomic—using a unified approach. Trained on 2.7M prokaryotic and phage genomes, encompassing 300B nucleotide tokens, Evo delivered single-nucleotide resolution across many biological evolution and function tasks.
At the core of Evo’s success is its innovative
StripedHyena
architecture (Figure 1), a hybrid model combining 29 Hyena layers, a new type of deep learning architecture designed to handle long sequences of information without relying on traditional attention mechanisms that are common to Transformer architectures. Instead it uses a combination of convolutional filters and gates.
This design overcame the limitations of traditional Transformer models, enabling Evo to handle long contexts of up to 131,072 tokens efficiently. The result was a model capable of connecting small sequence changes to system-wide and organism-level impacts, bridging the gap between molecular biology and evolutionary genomics.
Figure 1
.
Evo and Evo 2 AI model architecture
Evo’s predictive capabilities set new standards for biological modeling. It achieved competitive performance in several zero-shot tasks, including predicting the fitness effects of mutations on proteins, non-coding RNAs, and regulatory DNA, providing invaluable insights for synthetic biology and precision medicine.
Evo also demonstrated remarkable generative capabilities, designing functional CRISPR-Cas systems and transposons. These outputs were validated experimentally, proving that Evo could predict and design novel biological systems with real-world utility.
Evo represents a notable advancement in
integrating multimodal and multiscale biological understanding into a single model
. Its ability to generate genome-scale sequences and predict gene essentiality across entire genomes marked a leap forward in our capacity to analyze and engineer life.
Evo’s milestones were not just its technical achievements but also its vision. This unified framework combined biology’s vast complexity with cutting-edge AI to accelerate discovery and innovation in life sciences.
Learning the language of life across evolution
Evo 2 is the next generation of this line of research in genomic modeling, building on the success of Evo with expanded data, enhanced architecture, and superior performance.
Evo 2 can provide insights into three essential biomolecules—DNA, RNA, and protein— and all three domains of life: Eukarya, Prokarya, and Archaea. Trained on a dataset of 8.85T nucleotides from 15,032 eukaryotic genomes and 113,379 prokaryotic genomes, this training dataset covers diverse species, enabling unprecedented cross-species generalization and significantly broadening its scope compared to Evo, which focused solely on prokaryotic genomes.
Evo 2 uses a new and improved
StripedHyena 2
architecture, extended up to 40B parameters, enhancing the model’s training efficiency and ability to capture long-range dependencies with context lengths of 1M tokens. StripedHyena 2, thanks to its multihybrid design based on convolutions, trains significantly faster than Transformers and other hybrid models using linear attention or state-space models.
The largest Evo 2 model was trained using  2,048
NVIDIA H100 GPUs
using
NVIDIA DGX Cloud
on AWS. As part of
NVIDIA’s partnership with Arc
, they gained access to this high-performance, fully managed AI platform optimized for large-scale, distributed training with NVIDIA AI software and expertise.
These advances mark a significant increase from Evo’s 7B parameters and a 131,000-token context length, positioning Evo 2 as a leader in multimodal and multiscale biological modeling (Table 1).
Feature
Evo
Evo 2
Genomic Training Data
Bacterial
+ bacteriophage
(300B nucleotides)
All domains of life
+ bacteriophage
(9T nucleotides)
Model Parameters
7B
7B + 40B
Context Length
131,072 tokens
Up to 1,048,576 tokens
Modalities
DNA, RNA, protein
DNA, RNA, protein
Safety
Viruses of Eukaryotes excluded
Viruses of Eukaryotes excluded
Applications
Limited cross-species tasks
Broad cross-species applications
Table 1.
Key features of Evo 2 and Evo
Evo 2’s expanded training data and refined architecture empower it to excel across various biological applications. Its multimodal design integrates DNA, RNA, and protein data, enabling zero-shot performance on tasks like mutation impact prediction and genome annotation. Evo 2 also fundamentally improves Evo by including eukaryotic genomes, enabling deeper insights into human diseases, agriculture, and environmental science.
Evo 2’s predictive capabilities outperform specialized models across diverse tasks:
Variant impact analysis
: Achieves state-of-the-art accuracy in predicting the functional effects of mutations across species zero-shot, including human and non-coding variants.
Gene essentiality
: Identifies essential genes in prokaryotic and eukaryotic genomes, validated against experimental datasets, bridging the gap between molecular and systems biology tasks.
Generative capabilities
: Designs complex biological systems, such as genome-scale prokaryotic and eukaryotic sequences, and the controllable design of chromatin accessibility, demonstrating new capabilities for biological design with real-world applicability.
Using the NVIDIA Evo 2 NIM microservice
The
NVIDIA Evo 2 NIM microservice
is useful for generating a variety of biological sequences, with an API that provides settings to adjust tokenization, sampling, and temperature parameters:
# Define JSON example human L1 retrotransposable element sequence
example = {
 
# nucleotide sequence to be analyzed  
        "sequence": "GAATAGGAACAGCTCCGGTCTACAGCTCCCAGCGTGAGCGACGCAGAAGACGGTGATTTCTGCATTTCCATCTGAGGTACCGGGTTCATCTCACTAGGGAGTGCCAGACAGTGGGCGCAGGCCAGTGTGTGTGCGCACCGTGCGCGAGCCGAAGCAGGGCGAGGCATTGCCTCACCTGGGAAGCGCAAGGGGTCAGGGAGTTCCCTTTCCGAGTCAAAGAAAGGGGTGATGGACGCACCTGGAAAATCGGGTCACTCCCACCCGAATATTGCGCTTTTCAGACCGGCTTAAGAAACGGCGCACCACGAGACTATATCCCACACCTGGCTCAGAGGGTCCTACGCCCACGGAATC", 
        "num_tokens": 102, # number of tokens to generate
        "top_k": 4, # only predict top 4 most likely outcomes per token
        "top_p": 1.0, # include 100% cumulative prob results in sampling
        "temperature": 0.7, # add variability (creativity) to predictions
        "
": True, # enable more diverse outputs
        "enable_logits": False, # disable raw model output (logits)
}

# Retrieve the API key from the environment
key = os.getenv("NVCF_RUN_KEY")

# Send the example sequence and parameters to the Evo 2 API
r = requests.post(

        # Example URL for the Evo 2 model API.
        url=os.getenv("URL","https://health.api.nvidia.com/v1/biology/arc/evo2-40b/generate")
        
        # Authorization headers to authenticate with the API
        headers={"Authorization": f"Bearer {key}"},
        
        # The data payload (sequence and parameters) sent as JSON
        json=example,
)
For more information about the API output for various prompts, see the
NVIDIA BioNeMo Framework
documentation.
Evo 2 can also be fine-tuned using the open-source
NVIDIA BioNeMo Framework
, which offers robust tools for adapting pretrained models such as Evo 2 to specialized tasks in BioPharma:
# Prepare raw sequence data for training based on a YAML config file 
   preprocess_evo2 -c data_preproc_config.yaml

# Trains the Evo 2 model with preprocessed data and parallelism across multiple GPUs   
torchrun --nproc-per-node=8 --no-python train_Evo 2 -d data_train_config.yaml --num-nodes=1 --devices=8 --max-steps=100 --val-check-interval=25 --experiment-dir=/workspace/bionemo2/model/checkpoints/example --seq-length=8192 --tensor-parallel-size=4 --pipeline-model-parallel-size=1 --context-parallel-size=2 --sequence-parallel --global-batch-size=8 --micro-batch-size=1 --model-size=7b --fp8 --tflops-callback

# Optional Fine-tuning: Add this argument to start from a pretrained model  
# --ckpt-dir=/path/to/pretrained_checkpoint
Evo 2 and the future of AI in biology
AI is poised to rapidly transform biological research, enabling breakthroughs previously thought to be decades away. Evo 2 represents a significant leap forward in this evolution, introducing a genomic foundation model capable of analyzing and generating DNA, RNA, and protein sequences at unprecedented scales.
While Evo excelled in predicting mutation effects and gene expression in prokaryotes, the capabilities of Evo 2 are much broader, with enhanced cross-species generalization, making it a valuable tool for studying eukaryotic biology, human diseases, and evolutionary relationships.
Evo 2’s ability to perform zero-shot tasks, from identifying genes that drive cancer risk to designing complex biomolecular systems, showcases its versatility. Including long-context dependencies enables it to uncover patterns across genomes, providing multi-modal and multi-scale insights that are pivotal for advancements in precision medicine, agriculture, and synthetic biology.
As the field moves forward, models like Evo 2 set the stage for a future where AI deciphers life’s complexity and is also used to design new useful biological systems. These advancements align with broader trends in AI-driven science, where foundational models are tailored to domain-specific challenges, unlocking previously unattainable capabilities. Evo 2’s contributions signal a future where AI becomes an indispensable partner in decoding, designing, and reshaping the living world.
For more information about Evo 2, see the
technical report published by the Arc Institute
. Evo 2 is also available within the NVIDIA BioNeMo platform.
Acknowledgments
We’d like to thank the following contributors to the described research for their notable contributions to the ideation, writing, and figure design for this post:
Garyk Brixi
, genetics Ph.D. student at Stanford
Jerome Ku
, machine learning engineer working with the Arc Institute
Michael Poli
, founding scientist at Liquid AI and computer science Ph.D. student at Stanford
Greg Brockman
, co-founder and president of OpenAI
Eric Nguyen
, bioengineering Ph.D. student at Stanford
Brandon Yang
, co-founder of Cartesia AI and computer science Ph.D. student at Stanford (on leave)
Dave Burke
, chief technology officer at the Arc Institute
Hani Goodarzi
, core investigator at the Arc Institute and associate professor of biophysics and biochemistry at the University of California, San Francisco
Patrick Hsu
, co-founder of the Arc Institute, assistant professor of bioengineering, and Deb Faculty Fellow at the University of California, Berkeley
Brian Hie
, assistant professor of chemical engineering at Stanford University, Dieter Schwarz Foundation Stanford Data Science Faculty Fellow, innovation investigator at the Arc Institute, and leader at the Laboratory of Evolutionary Design at Stanford
利用 Evo 2 以全新规模跨进化研究生命生物分子的语言
AI 已从实验好奇心发展为生物学研究的驱动力。
深度学习算法、海量组学数据集和自动化实验室工作流程的融合使
科学家能够解决一度被认为棘手的问题
(从快速蛋白质结构预测到生成式药物设计)，从而增加了科学家对 AI 素养的需求。在这一势头下，我们正处于下一次范式转变的边缘：专为生物学打造的强大 AI 基础模型的出现。
这些新模型有望将不同的数据源 (基因组序列、RNA 和蛋白质组谱，在某些情况下还包括科学文献) 统一为分子、细胞和系统级别上对生命的统一、一致的理解。学习生物学的语言和结构为变革性应用打开了大门，例如更智能的药物发现、合理的酶设计和疾病机制阐明。
在我们为下一波 AI 驱动的突破做好准备之际，这些基础模型显然不仅能够加速进展，还将重新定义生物学研究的可能性。
使用/NVIDIA/bionemo-examples
示例 notebook
免费将 Evo 2 测试为 NVIDIA BioNeMo NIM 微服务
。
探索蛋白质设计的完整参考工作流程
。
立即开始在
BioNeMo 框架
中使用您的数据训练 Evo 2
及时了解 NVIDIA BioNeMo 平台的最新动态。
序列建模和设计从分子规模到基因组规模的飞跃
2024 年 11 月推出的
首个 Evo 模型
是基因组研究领域的一个突破性里程碑，它引入了能够分析和生成跨 DNA、RNA 和蛋白质的生物序列的基础模型。
在发布 Evo 时，大多数模型都被限制在单一模式或简短背景下运行，而众所周知，它能够使用统一的方法跨规模 (从分子到基因组) 操作。Evo 基于 270 万个原核细胞和噬菌体基因组 (包含 300 亿个核酸令牌) 进行训练，在许多生物进化和功能任务中提供了单核酸分辨率。
Evo 成功的核心是其创新的 StripedHyena 架构 (图 1)，这是一个结合了 29 个 Hyena 层的混合模型，这是一种新型深度学习架构，旨在处理长序列信息，而无需依赖 Transformer 架构中常见的传统注意机制。相反，它使用卷积过滤器和门的组合。
这种设计克服了传统 Transformer 模型的限制，使 Evo 能够高效处理多达 131,072 个令牌的长上下文。最终，该模型能够将微小的序列变化与系统级和有机体级的影响联系起来，弥合分子生物学与进化基因组学之间的差距。
图 1
.
.
.
Evo 和 Evo 2 AI 模型架构
Evo 的预测功能为生物建模树立了新的标准。它在多项零样本任务中取得了具有竞争力的表现，包括预测突变对蛋白质、非编码 RNAs 和调控 DNA 的适应性影响，为合成生物学和精准医学提供宝贵见解。
Evo 还展示了非凡的生成功能，设计了功能齐全的 CRISPR-Cas 系统和转座子。这些输出经过实验验证，证明了 Evo 可以预测和设计具有真实效用的新型生物系统。
Evo
代表着将多模态和多尺度生物理解集成到单个模型中的显著进步
。它能够生成基因组级序列并预测整个基因组的基因本质，这标志着我们分析和工程生命的能力实现了飞跃。
Evo 的里程碑式发展不仅体现在技术成就上，还体现在愿景上。这一统一框架将生物学的庞大复杂性与尖端 AI 相结合，加速了生命科学领域的发现和创新。
学习进化过程中的生命语言
Evo 2 是基因组建模领域这一系列研究的新一代产品，基于 Evo 在扩展数据、增强架构和卓越性能方面取得的成功而构建。
Evo 2 可以深入了解三种基本生物分子 (DNA、RNA 和蛋白质) 以及生命的所有三个领域：真核生物 (Eukarya)、原核生物 (Prokarya) 和古菌 (Archaea)。此训练数据集基于来自 15,032 个真核基因组和 113,379 个原核基因组的 8.85T 核酸数据集进行训练，涵盖不同物种，与仅关注原核基因组的 Evo 相比，实现了前所未有的跨物种泛化，并显著拓宽了其范围。
Evo 2 使用经过改进的全新
StripedHyena 2
架构，该架构可将参数扩展至 40B 个，从而提高模型的训练效率，以及使用 1M 个令牌的上下文长度捕获远程依赖项的能力。
StripedHyena 2
采用基于卷积的多混合设计，其训练速度明显快于 Transformers 和其他使用线性注意力或状态空间模型的混合模型。
最大的 Evo 2 模型使用 AWS 上的
NVIDIA DGX Cloud
使用 2,048 个
NVIDIA H100 GPUs
进行训练。作为
NVIDIA 与 Arc 合作
的一部分，他们获得了对这个高性能、完全托管的 AI 平台的访问权限，该平台利用 NVIDIA AI 软件和专业知识针对大规模分布式训练进行了优化。
这些进步标志着 Evo 的 7B 参数和 131,000 个令牌的上下文长度有了显著增加，使 Evo 2 成为多模态和多尺度生物建模领域的领导者 (表 1)。
特征
Evo
Evo 2
基因组训练数据
细菌 + bacteriophage(300B 核酸)
所有生命领域 + bacteriophage(9T nucleotides)
模型参数
70 亿
70 亿 400 亿
上下文长度
131072 个令牌
最多 1048576 个令牌
模式
DNA、RNA、蛋白质
DNA、RNA、蛋白质
安全性
不包括真核生物病毒
不包括真核生物病毒
应用
跨物种任务受限
广泛的跨物种应用
表 1、Evo 2 和 Evo 的主要特性
Evo 2 的扩展训练数据和优化架构使其能够在各种生物应用中表现卓越。其多模态设计集成了 DNA、RNA 和蛋白质数据，可在执行突变影响预测和基因组标注等任务时实现零采样性能。Evo 2 还通过纳入真核基因组从根本上改进了 Evo，使人们能够更深入地了解人类疾病、农业和环境科学。
Evo 2 的预测能力优于各种任务的专业模型：
变体影响分析
：在零样本预测物种突变 (包括人类和非编码变体) 的功能性影响方面实现出色的准确性。
基因本质
：识别原核和真核基因组中的基本基因，并通过实验数据集进行验证，弥合分子和系统生物学任务之间的差距。
生成功能
：设计复杂的生物系统（例如基因组级的原核细胞序列和真核细胞序列），以及染色质可访问性的可控设计，展示具有现实世界适用性的生物设计新功能。
使用 NVIDIA Evo 2 NIM 微服务
NVIDIA Evo 2 NIM 微服务
可用于生成各种生物序列，其 API 可提供用于调整标记化、采样和温度参数的设置：
# Define JSON example human L1 retrotransposable element sequence
example = {
 
# nucleotide sequence to be analyzed  
        "sequence": "GAATAGGAACAGCTCCGGTCTACAGCTCCCAGCGTGAGCGACGCAGAAGACGGTGATTTCTGCATTTCCATCTGAGGTACCGGGTTCATCTCACTAGGGAGTGCCAGACAGTGGGCGCAGGCCAGTGTGTGTGCGCACCGTGCGCGAGCCGAAGCAGGGCGAGGCATTGCCTCACCTGGGAAGCGCAAGGGGTCAGGGAGTTCCCTTTCCGAGTCAAAGAAAGGGGTGATGGACGCACCTGGAAAATCGGGTCACTCCCACCCGAATATTGCGCTTTTCAGACCGGCTTAAGAAACGGCGCACCACGAGACTATATCCCACACCTGGCTCAGAGGGTCCTACGCCCACGGAATC", 
        "num_tokens": 102, # number of tokens to generate
        "top_k": 4, # only predict top 4 most likely outcomes per token
        "top_p": 1.0, # include 100% cumulative prob results in sampling
        "temperature": 0.7, # add variability (creativity) to predictions
        "
": True, # enable more diverse outputs
        "enable_logits": False, # disable raw model output (logits)
}

# Retrieve the API key from the environment
key = os.getenv("NVCF_RUN_KEY")

# Send the example sequence and parameters to the Evo 2 API
r = requests.post(

        # Example URL for the Evo 2 model API.
        url=os.getenv("URL","https://health.api.nvidia.com/v1/biology/arc/evo2-40b/generate")
        
        # Authorization headers to authenticate with the API
        headers={"Authorization": f"Bearer {key}"},
        
        # The data payload (sequence and parameters) sent as JSON
        json=example,
)
有关各种提示的 API 输出的更多信息，请参阅
NVIDIA BioNeMo 框架文档
。
此外，还可以使用开源
NVIDIA BioNeMo Framework
对 Evo 2 进行微调，该框架提供可靠的工具，可根据 BioPharma 中的专业任务调整预训练模型（例如 Evo 2）：
# Prepare raw sequence data for training based on a YAML config file 
   preprocess_evo2 -c data_preproc_config.yaml

# Trains the Evo 2 model with preprocessed data and parallelism across multiple GPUs   
torchrun --nproc-per-node=8 --no-python train_Evo 2 -d data_train_config.yaml --num-nodes=1 --devices=8 --max-steps=100 --val-check-interval=25 --experiment-dir=/workspace/bionemo2/model/checkpoints/example --seq-length=8192 --tensor-parallel-size=4 --pipeline-model-parallel-size=1 --context-parallel-size=2 --sequence-parallel --global-batch-size=8 --micro-batch-size=1 --model-size=7b --fp8 --tflops-callback

# Optional Fine-tuning: Add this argument to start from a pretrained model  
# --ckpt-dir=/path/to/pretrained_checkpoint
Evo 2 和生物学领域的 AI 未来
AI 势必会迅速改变生物学研究，实现之前人们认为需要数十年才能实现的突破。Evo 2 代表了这一变革的重大飞跃，它引入了基因组基础模型，能够以超大规模分析和生成 DNA、RNA 和蛋白质序列。
虽然 Evo 在预测原核生物的突变效应和基因表达方面表现优异，但 Evo 2 的功能更为广泛，并增强了跨物种泛化，这使其成为研究真核生物学、人类疾病和进化关系的宝贵工具。
从识别导致癌症风险的基因到设计复杂的生物分子系统，Evo 2 执行零样本击任务的能力充分体现了其通用性。借助长上下文依赖项，AI 能够揭示跨基因组的模式，提供对精准医学、农业和合成生物学的进步至关重要的多模态和多尺度见解。
随着该领域的发展，像 Evo 2 这样的模型为 AI 解读生命复杂性的未来奠定了基础，同时还用于设计新的有用的生物系统。这些进步与 AI 驱动的科学领域的更广泛趋势是一致的，在这些趋势中，基础模型针对特定领域的挑战进行了定制，解锁了以前无法实现的功能。Evo 2 的贡献标志着 AI 将成为解码、设计和重塑生存世界不可或缺的合作伙伴。
有关 Evo 2 的更多信息，请参阅 Arc Institute 发布的技术报告。Evo 2 也可在 NVIDIA BioNeMo 平台中使用。
致谢
在此，
我们要感谢以下参与所述研究的人员，感谢他们为本文的构思、写作和图形设计做出的杰出贡献
：
Garyk Brixi
，斯坦福大学遗传学博士生
与 Arc Institute 合作的机器学习工程师
Jerome Ku
Michael Poli
，Liquid AI 的创始科学家兼斯坦福大学计算机科学博士生
Greg Brockman
，OpenAI 联合创始人兼总裁
Eric Nguyen，
斯坦福大学生物工程博士生
Brandon Yang
，Cartesia AI 联合创始人兼斯坦福大学计算机科学博士生 (休假中)
Dave Burke
，Arc Institute 首席
技术官
Hani Goodarzi
，Arc Institute 核心研究员，加州大学旧金山分校生物物理学和生物化学副教授
Patrick Hsu
，Arc Institute 联合创始人、生物工程助理教授兼加州大学伯克利分校 Deb 教职研究员
Brian Hie
– 斯坦福大学化学工程助理教授、Dieter Schwarz 基金会斯坦福大学数据科学教职人员、Arc Institute 创新研究员、斯坦福大学进化设计实验室负责人