A relatively new startup called EvolutionaryScale has secured a massive tranche of cash to build AI models to generate novel proteins for scientific research.

一家相对较新的初创公司EvolutionaryScale获得了大量资金，用于构建人工智能模型，以产生用于科学研究的新型蛋白质。

EvolutionaryScale today announced that it raised $142 million in a seed round led by ex-GitHub CEO Nat Friedman, Daniel Gross and Lux Capital with participation from Amazon and NVentures, Nvidia’s corporate venture arm. The company also released ESM3, an AI model it describes as a “frontier model” for biology — one that can create proteins for use cases like drug discovery and materials science..

EvolutionaryScale今天宣布，它在前GitHub首席执行官纳特·弗里德曼（NatFriedman）、丹尼尔·格罗斯（DanielGross）和勒克斯资本（LuxCapital）领导的种子轮融资中筹集了1.42亿美元，亚马逊（Amazon）和Nvidia的企业风险投资子公司NVentures也参与了这轮融资。该公司还发布了ESM3，这是一种人工智能模型，它被描述为生物学的“前沿模型”，可以为药物发现和材料科学等用例创建蛋白质。

“ESM3 takes a step toward a future of biology where AI is a tool to engineer from first principles, the way we engineer structures, machines, and microchips and write computer programs,” EvolutionaryScale co-founder and chief scientist Alexander Rives said in a statement.

进化尺度（EvolutionaryScale）联合创始人兼首席科学家亚历山大·里维斯（AlexanderRives）在一份声明中表示：“ESM3朝着生物学的未来迈出了一步，在生物学中，人工智能是一种从第一原理进行工程设计的工具，即我们设计结构、机器和微芯片以及编写计算机程序的方式。”

Rives, along with Tom Secru and Sal Candido, began developing generative AI models to explore proteins while at Meta’s AI research lab, FAIR, in 2019. After their team was disbanded, Rives, Secru and Candido left Meta to continue building on the work they’d started.

2019年，里维斯（Rives）与汤姆·塞克鲁（TomSecru）和萨尔·坎迪多（SalCandido）在梅塔（Meta）的人工智能研究实验室（FAIR）开始开发生成人工智能模型来探索蛋白质。他们的团队解散后，里维斯、塞克鲁和坎迪多离开梅塔继续他们开始的工作。

Characterizing proteins can reveal the mechanisms of a disease, including ways to slow it or reverse it, while creating proteins can lead to entirely new classes of drugs, tools and therapeutics. But the current process for designing proteins in the lab is costly, both from a computational and human resource standpoint.

表征蛋白质可以揭示疾病的机制，包括减缓或逆转疾病的方法，而创造蛋白质可以产生全新的药物，工具和治疗方法。但从计算和人力资源的角度来看，目前在实验室设计蛋白质的过程成本高昂。

Designing a protein entails coming up with a structure that could plausibly perform a task inside the body or a product, then finding a protein sequence — the sequence of amino acids that make up a protein — likely to “fold” into the structure. Proteins must correctly fold into three-dimensional shapes in order to carry out their intended function..

设计一种蛋白质需要找到一种结构，该结构可以合理地在体内或产品中执行任务，然后找到一个蛋白质序列-构成蛋白质的氨基酸序列-可能“折叠”到该结构中。

Trained on data set of 2.78 billion proteins, ESM3 can “reason over” the sequence, structure and function of proteins, Rives says — enabling the model to generate new proteins a la Google DeepMind’s AlphaFold. EvolutionaryScale is making the full 98-billion-parameter model available for non-commercial use through its cloud Forge developer platform and releasing a smaller version of the model for offline use..

Rives说，ESM3通过对27.8亿个蛋白质数据集的训练，可以“推理”蛋白质的序列、结构和功能，从而使该模型能够产生类似谷歌DeepMind的AlphaFold的新蛋白质。EvolutionaryScale正在通过其cloud Forge开发平台使完整的980亿参数模型可用于非商业用途，并发布该模型的较小版本以供离线使用。

EvolutionaryScale claims that it used ESM3 to generate a new variant of green fluorescent protein (GFP), the protein responsible for the glowing of jellyfish and luminescent colors in coral. A preprint paper on the company’s website details its work.

EvolutionaryScale声称，它使用ESM3产生了一种新的绿色荧光蛋白（GFP）变体，这种蛋白负责水母发光和珊瑚发光。

The fluorescent protein ‘esmGFP,’ created with EvolutionaryScale’s ESM3.Image Credits: EvolutionaryScale

荧光蛋白“esmGFP”，由EvolutionaryScale的ESM3创建。图片来源：EvolutionaryScale

“We’ve been working on this for a long time, and we’re excited to share it with the scientific community and see what they do with it,” Rives continued.

里夫斯继续说道：“我们已经在这方面工作了很长时间，我们很高兴能与科学界分享它，看看他们用它做什么。”

EvolutionaryScale isn’t a charity, of course — the roughly-20-employee company tells TechCrunch that it plans to make money through a combination of partnerships, usage fees and revenue sharing. EvolutionaryScale might work with pharmaceutical companies to integrate ESM3 into their workflows, for example, or revenue-share with researchers for breakthrough discoveries commercialized using ESM3..

当然，EvolutionaryScale不是慈善机构——这家大约有20名员工的公司告诉TechCrunch，它计划通过合作伙伴关系、使用费和收入分享相结合的方式赚钱。例如，EvolutionaryScale可能会与制药公司合作，将ESM3整合到他们的工作流程中，或者与研究人员分享使用ESM3商业化的突破性发现的收入。

To this end, EvolutionaryScale says that it’ll soon bring ESM3 and its derivatives to select AWS customers via AWS’ SageMaker AI dev platform, Bedrock AI platform and HealthOmics service. ESM3 will also be available to select customers using NVIDIA’s NIM microservices, supported with an Nvidia enterprise software license.

为此，EvolutionaryScale表示，它将很快通过AWS的SageMaker AI dev平台、Basket AI平台和HealthOmics服务，将ESM3及其衍生物引入AWS客户的选择中。ESM3也可用于选择使用NVIDIA的NIM微服务的客户，该服务由NVIDIA企业软件许可证支持。

EvolutionaryScale says that both AWS and Nvidia customers will be able to fine-tune ESM3 using their own data if they wish.

EvolutionaryScale表示，如果愿意，AWS和Nvidia客户都可以使用自己的数据对ESM3进行微调。

It could be a while before EvolutionaryScale turns a profit. In the company’s pitch deck, a copy of which Forbes managed to obtain last August, EvolutionaryScale repeatedly emphasized that it could take a decade for generative AI models to help design therapies. The firm will also have to fend off competition like DeepMind’s spinoff Isomorphic Labs, which already has contracts with big pharma companies, as well as Insitro, publicly-traded Recursion and Inceptive.

进化规模（EvolutionaryScale）实现盈利可能需要一段时间。《福布斯》（Forbes）去年8月成功获得了该公司的广告牌，进化规模（EvolutionaryScale）反复强调，生成性人工智能模型可能需要十年才能帮助设计疗法。该公司还必须抵御竞争，如DeepMind的派生同构实验室，该实验室已经与大型制药公司以及Insitro、上市递归和Inceptive签订了合同。

EvolutionaryScale’s big bet is scaling up its model training to incorporate data beyond proteins and create a general-purpose AI model for biotech applications.

EvolutionaryScale的大赌注是扩大其模型训练，以整合蛋白质以外的数据，并为生物技术应用创建通用AI模型。

“The incredible pace of new AI advances is being driven by increasingly large models, increasingly large data sets and increasing computational power,” an EvolutionaryScale spokesperson said. “The same holds true in biology. In research over the last five years, the ESM team has explored scaling in biology.

进化尺度（EvolutionaryScale）发言人表示：“越来越大的模型、越来越大的数据集和越来越高的计算能力正在推动人工智能取得令人难以置信的新进展。”。“生物学也是如此。在过去五年的研究中，ESM团队探索了生物学中的缩放。

We find that as language models scale, they develop an understanding of the underlying principles of biology, and discover biological structure and function.”.

我们发现，随着语言模型的规模扩大，他们对生物学的基本原理有了理解，并发现了生物学的结构和功能。”

Sounds wildly ambitious to this reporter — but having deep-pocketed investors surely helps.

这位记者听起来雄心勃勃，但拥有资金雄厚的投资者肯定会有所帮助。