Scientists everywhere can now access Evo 2, a powerful new
foundation model
that understands the genetic code for all domains of life. Unveiled today as the largest publicly available AI model for genomic data, it was built on the NVIDIA DGX Cloud platform in a collaboration led by nonprofit biomedical research organization Arc Institute and Stanford University.
Evo 2 is available to global developers on the
NVIDIA BioNeMo platform
, including as an NVIDIA NIM microservice for easy, secure AI deployment.
Trained on an enormous dataset of nearly 9 trillion nucleotides — the building blocks of DNA and RNA — Evo 2 can be applied to biomolecular research applications including predicting the form and function of proteins based on their genetic sequence, identifying novel molecules for healthcare and industrial applications, and evaluating how gene mutations affect their function.
“Evo 2 represents a major milestone for generative genomics,” said Patrick Hsu, Arc Institute cofounder and core investigator, and an assistant professor of bioengineering at the University of California, Berkeley. “By advancing our understanding of these fundamental building blocks of life, we can pursue solutions in healthcare and environmental science that are unimaginable today.”
The NVIDIA
NIM microservice for Evo 2
enables users to generate a variety of biological sequences, with settings to adjust model parameters. Developers interested in fine-tuning Evo 2 on their proprietary datasets can download the model through the open-source
NVIDIA BioNeMo Framework
, a collection of accelerated computing tools for biomolecular research.
“Designing new biology has traditionally been a laborious, unpredictable and artisanal process,” said Brian Hie, assistant professor of chemical engineering at Stanford University, the Dieter Schwarz Foundation Stanford Data Science Faculty Fellow and an Arc Institute innovation investigator. “With Evo 2, we make biological design of complex systems more accessible to researchers, enabling the creation of new and beneficial advances in a fraction of the time it would previously have taken.”
Enabling Complex Scientific Research
Established in 2021 with $650 million from its founding donors, Arc Institute empowers researchers to tackle long-term scientific challenges by providing scientists with multiyear funding — letting scientists focus on innovative research instead of grant writing.
Its core investigators receive state-of-the-art lab space and funding for eight-year, renewable terms that can be held concurrently with faculty appointments with one of the institute’s university partners, which include Stanford University, the University of California, Berkeley, and the University of California, San Francisco.
By combining this unique research environment with accelerated computing expertise and resources from NVIDIA, Arc Institute’s researchers can pursue more complex projects, analyze larger datasets and more quickly achieve results. Its scientists are focused on disease areas including cancer, immune dysfunction and neurodegeneration.
NVIDIA accelerated the Evo 2 project by giving scientists access to 2,000 NVIDIA H100 GPUs via
NVIDIA DGX Cloud
on AWS. DGX Cloud provides short-term access to large compute clusters, giving researchers the flexibility to innovate. The fully managed AI platform includes
NVIDIA BioNeMo
, which features optimized software in the form of NVIDIA NIM microservices and NVIDIA BioNeMo Blueprints.
NVIDIA researchers and engineers also collaborated closely on AI scaling and optimization.
Applications Across Biomolecular Sciences
Evo 2 can provide insights into DNA, RNA and proteins. Trained on a wide array of species across domains of life — including plants, animals and bacteria — the model can be applied to scientific fields such as healthcare, agricultural biotechnology and materials science.
Evo 2 uses a novel model architecture that can process lengthy sequences of genetic information, up to 1 million tokens. This widened view into the genome could unlock scientists’ understanding of the connection between distant parts of an organism’s genetic code and the mechanics of cell function, gene expression and disease.
“A single human gene contains thousands of nucleotides — so for an AI model to analyze how such complex biological systems work, it needs to process the largest possible portion of a genetic sequence at once,” said Hsu.
In healthcare and drug discovery, Evo 2 could help researchers understand which gene variants are tied to a specific disease — and design novel molecules that precisely target those areas to treat the disease. For example, researchers from Stanford and the Arc Institute found that in tests with BRCA1, a gene associated with breast cancer, Evo 2 could predict with 90% accuracy whether previously unrecognized mutations would affect gene function.
In agriculture, the model could help tackle global food shortages by providing insights into plant biology and helping scientists develop varieties of crops that are more climate-resilient or more nutrient-dense. And in other scientific fields, Evo 2 could be applied to design biofuels or engineer proteins that break down oil or plastic.
“Deploying a model like Evo 2 is like sending a powerful new telescope out to the farthest reaches of the universe,” said Dave Burke, Arc’s chief technology officer. “We know there’s immense opportunity for exploration, but we don’t yet know what we’re going to discover.”
Read more about Evo 2 on the
NVIDIA Technical Blog
and in
Arc’s technical report
.
See
notice
regarding software product information.
Categories:
Deep Learning
|
Generative AI
Tags:
Arc Institute
|
Artificial Intelligence
|
Healthcare and Life Sciences
|
NVIDIA DGX Cloud
|
Open Source
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Science
|
Social Impact
全球各地的科學家現在可以使用瞭解所有生命領域的遺傳密碼的強大全新
基礎模型
Evo 2。Evo 2 是由非營利生物醫學研究組織 Arc Institute 與史丹佛大學合作，在 NVIDIA DGX Cloud 平台上所開發，是目前規模最大的公開基因組資料人工智慧（AI）模型。
Evo 2 在
NVIDIA BioNeMo 平台
上供全球開發人員使用，包括以 NVIDIA NIM 微服務的方式進行簡易、安全的部署 AI。
Evo 2 模型使用近 9 兆個核苷酸（DNA 和 RNA 的組成部分）所組成的龐大資料集訓練出，可用於生物分子研究應用，包括根據基因序列預測蛋白質的形式和功能、識別用於醫療保健和工業應用的新型分子，以及評估基因突變如何影響其功能。
Arc Institute 共同創辦人暨核心研究員、加州大學柏克萊分校生物工程助理教授徐安祺（Patrick Hsu）表示：「Evo 2 代表著生成式基因組學的重要里程碑。透過推進我們對這些生命基本構成元素的了解，我們能在醫療保健和環境科學領域尋求目前難以想像的解決方案。」
適用於 Evo 2 的 NVIDIA NIM 微服務
可讓使用者產生各種生物序列，並能設定裡調整模型參數。對於想要使用自己專屬資料集來微調 Evo 2 的開發人員，可以透過開源的
NVIDIA BioNeMo 框架
下載模型，該框架是一系列用於生物分子研究的加速運算工具。
史丹佛大學化學工程助理教授、Dieter Schwarz 基金會史丹佛大學資料科學系研究員，同時也是 Arc Institute 創新研究員的 Brian Hie 表示：「設計新的生物學傳統上是一個費力、難以預測且需要用到大量人工的過程。有了 Evo 2，我們讓研究人員更容易進行複雜系統的生物設計，只要用到比以前短上不少的時間，就能創造出有益的新進展。」
推動複雜的科學研究
Arc Institute 在 6.5 億美元的捐助資金下於 2021 年成立，透過資助科學家多年期資金，讓科學家專注於創新研究，解決科學領域長期面對的難題，而不用忙於申請資金。
Arc Institute 的核心研究人員可以獲得最先進的實驗室，以及為期八年且可續約的資金，並可同時在與該單位合作的大學之一任教，包括史丹佛大學、加州大學柏克萊分校和加州大學舊金山分校。
透過結合這個獨特的研究環境與 NVIDIA 的加速運算專業技術與資源，Arc Institute 的研究人員可以進行更複雜的專案、分析更大的資料集，並且更快的取得成果。該單位的科學家專注於癌症、免疫功能障礙和神經退化性疾病等領域。
NVIDIA 透過 Amazon Web Services（AWS）上的
NVIDIA DGX Cloud
讓科學家們能夠使用 2,000 個 NVIDIA H100 GPU，加快進行 Evo 2 計畫。DGX Cloud 提供短期使用大型運算叢集的能力，使研究人員得以靈活進行創新。這個完全託管的 AI 平台包含
NVIDIA BioNeMo
，以 NVIDIA NIM 微服務和 NVIDIA BioNeMo Blueprints 的形式提供最佳化的軟體。
NVIDIA 研究人員與工程師同樣在 AI 擴展與最佳化方面密切合作。
應用於生物分子科學
Evo 2 可以提供對 DNA、RNA 和蛋白質的深入瞭解。該模型經過對植物、動物和細菌等生命領域各個物種的訓練，可以應用於醫療保健、農業生物技術和材料科學等科學領域。
Evo 2 採用新穎的模型架構，可以處理長序列的遺傳資訊，最多可達 100 萬個詞元（token）。這種對基因組的更深認識可以讓科學家明白生物遺傳密碼裡距離較遠的部分與細胞功能、基因表現和疾病機制之間的關係。
徐安祺表示：「一個人類基因裡有著數千個核苷酸，如果要讓 AI 模型分析這麼複雜的生物系統如何運作，就必須一次處理基因序列中的最大可能部分。」
在醫療保健和藥物探索方面，Evo 2 模型可以幫助研究人員了解哪些基因變異與特定疾病有關，並設計出新型分子，精確地針對這些區域來治療疾病。像是史丹佛大學與 Arc Institute 的研究人員發現，在測試與乳癌有關的基因 BRCA1 時，Evo 2 能以 90% 的準確率預測先前未識別的突變是否會影響基因功能。
在農業方面，這個模型可以提出對植物生物學的洞察，幫助科學家開發更能適應氣候或是更營養的農作物品種，從而幫助解決全球糧食短缺的問題。而在其他科學領域，Evo 2 可應用於設計生物燃料或工程蛋白質，以分解油脂或塑膠。
Arc Institute 技術長 Dave Burke 表示：「部署像 Evo 2 這樣的模型，就如同將一具強大的新望遠鏡送往宇宙最遙遠的地方。我們知道這裡有著無限的探索機會，但是我們還不知道會發現什麼。」
如欲獲得更多有關 Evo 2 的資訊，請參閱
NVIDIA 技術部落格
與
Arc 的技術報告
。
請參閱有關軟體產品資訊的
公告
。
Categories:
深度學習與人工智慧
|
生成式人工智慧
Tags:
Arc Institute
|
Artificial Intelligence
|
Healthcare and Life Sciences
|
NVIDIA DGX Cloud
|
Open Source
|
Science
|
Social Impact