Evan Feinberg, PhD, founder and CEO of Genesis Therapeutics

Evan Feinberg博士，Genesis Therapeutics创始人兼首席执行官

In the beginning, Genesis Therapeutics spun out from the Stanford University lab of Vijay Pande, PhD, where Evan Feinberg, PhD, then a graduate student, co-invented and co-authored key peer-reviewed papers detailing deep learning technologies.

起初，Genesis Therapeutics是从斯坦福大学Vijay Pande博士实验室衍生出来的，当时是研究生的Evan Feinberg博士在那里共同发明并共同撰写了详细介绍深度学习技术的关键同行评审论文。

Notable among them was PotentialNet, the influential neural network algorithm that pioneered the use of novel graph neural networks for molecular property prediction, specifically protein–ligand binding affinity. Feinberg, Pande, and colleagues demonstrated PotentialNet’s performance in potency prediction, further validated through a collaboration between Stanford and Merck Research Laboratories, where Feinberg served as a deep learning consultant before launching Genesis..

其中值得注意的是PotentialNet，这是一种有影响力的神经网络算法，开创了使用新型图形神经网络进行分子特性预测，特别是蛋白质-配体结合亲和力的先河。范伯格（Feinberg）、潘德（Pande）及其同事展示了PotentialNet在效力预测方面的表现，并通过斯坦福大学（Stanford）和默克研究实验室（Merck Research Laboratories）之间的合作进一步验证了这一点，范伯格（Feinberg）在启动Genesis之前曾担任深度学习顾问。。

Genesis was founded in 2019, and a year later won a $52 million Series A financing. The company has grown since then to raise more than $300 million. Most of that consists of a $200 million Series B financing round completed in 2023. Its investors included NVentures, the venture capital arm of Nvidia, the Silicon Valley-based microprocessing giant that has expanded its market-leading footprint in AI chips to the life industries that include the life sciences..

Genesis成立于2019年，一年后获得了5200万美元的a系列融资。自那时以来，该公司已发展壮大，筹集资金超过3亿美元。其中大部分包括2023年完成的2亿美元B轮融资。其投资者包括Nvidia的风险投资子公司NVentures。Nvidia是一家总部位于硅谷的微处理巨头，已将其在AI芯片领域的市场领先地位扩展到包括生命科学在内的生命行业。。

NVentures recently raised its stake in Genesis by investing what Genesis founder and CEO Feinberg, said was an undisclosed “incremental additional amount” in his company.

最近，NVentures通过投资Genesis创始人兼首席执行官范伯格（Feinberg）所说的未披露的公司“额外增量”，提高了其在Genesis的股份。

Through its collaboration with Nvidia, Genesis is working to accelerate the development of its AI platform, Genesis Exploration of Molecular Space (GEMS). GEMS is designed to generate and optimize molecules for complex targets by integrating proprietary AI methods that include language models, diffusion models, and physical machine learning (ML) simulations..

。GEMS旨在通过集成专有的AI方法（包括语言模型，扩散模型和物理机器学习（ML）模拟）来生成和优化复杂目标的分子。。

The additional financing from NVentures is intended to further the capabilities of Genesis’ physical AI platform for structure-driven drug design by applying Nvidia’s expertise to make computation more efficient for several neural network architectures relevant to drug discovery.

来自NVentures的额外融资旨在通过应用Nvidia的专业知识，使与药物发现相关的几种神经网络架构的计算效率更高，从而进一步提高Genesis物理AI平台在结构驱动药物设计中的能力。

“Nvidia is the leader in many aspects of the AI stack, both in terms of hardware, but also the lower-level software layers on top of that hardware, whereas Genesis has been pioneering molecular AI as an intellectual area,” Feinberg told GEN Edge. “So, there’s a lot of very clear synergies between Nvidia’s comparative advantages and Genesis’s comparative advantages that make the combination more than the sum of the parts.”.

范伯格告诉GEN Edge：“Nvidia在人工智能堆栈的许多方面都处于领先地位，无论是在硬件方面，还是在硬件之上的较低级别软件层方面，而Genesis一直是分子人工智能领域的先驱。”。“因此，Nvidia的比较优势和Genesis的比较优势之间有着非常明显的协同作用，这使得两者的结合超过了部分的总和。”。

Optimizing neural networks

优化神经网络

The collaboration will, among other areas, encompass optimizing equivariant neural networks, which according to Genesis are valuable for handling 3D geometric data such as protein and small molecule structures.

除其他领域外，该合作将包括优化等变神经网络，根据Genesis的说法，这些神经网络对于处理蛋白质和小分子结构等3D几何数据很有价值。

Nvidia has consistently worked to accelerate computation through neural networks, both training the networks as well as running inference—using trained models to make predictions on new data—or deploying them in a real-world setting.

Nvidia一直致力于通过神经网络加速计算，既可以训练网络，也可以使用经过训练的模型进行推理，以对新数据进行预测，或者将其部署到现实环境中。

“For our field of molecular AI that Genesis has been pioneering for years, there are specific types of neural networks that are particularly useful. And that’s actually the continuation of a long trend in the space, where AI is not a monolith. There are many subfields of artificial intelligence that use related but distinct algorithms for learning.”.

“对于Genesis多年来一直开创的分子人工智能领域，有一些特定类型的神经网络特别有用。这实际上是空间中长期趋势的延续，人工智能并不是一块巨石。人工智能的许多子领域使用相关但不同的算法进行学习。”。

At Stanford, Feinberg, Pande, and one group of colleagues presented the PotentialNet family of graph convolutions in a 2018 paper in ACS Central Science. Two years later, another group of colleagues joined Feinberg and Pande in showing how, by representing each molecule explicitly as a graph, they achieved “to our knowledge, unprecedented accuracy in prediction of ADMET [absorption, distribution, metabolism, elimination, and toxicity] properties,” showing significant superiority of AI algorithms—a relative 52% and absolute 0.16 increase in R2 versus Random Forests in ADMET prediction—over the advanced ML used by Merck Research Laboratories in a paper published in the Journal of Medicinal Chemistry..

在斯坦福大学，范伯格（Feinberg），潘德（Pande）和一组同事在2018年ACS Central Science的一篇论文中介绍了潜在的图卷积家族。两年后，另一组同事与Feinberg和Pande一起展示了如何通过将每个分子明确表示为一个图表，他们实现了“据我们所知，在预测ADMET[吸收，分布，代谢，消除和毒性]特性方面前所未有的准确性”，显示了AI算法的显着优势-在ADMET预测中，相对于默克研究实验室在《药物化学杂志》上发表的一篇论文中使用的先进ML，R2相对于随机森林相对增加了52%，绝对增加了0.16。。

Vijay Pande, PhD, general partner at Andreessen Horowitz (a16z) and the founding general partner of a16z’s bio funds, where he leads the firm’s investments that cross biology, computer science, and engineering.

Vijay Pande博士，Andreessen Horowitz（a16z）普通合伙人，a16z生物基金的创始普通合伙人，他领导该公司在生物学、计算机科学和工程领域的投资。

Pande is now general partner at Andreessen Horowitz (a16z) and the founding general partner of a16z’s bio funds, where he leads the firm’s investments that cross biology, computer science, and engineering. Pande, who served as Feinberg’s PhD advisor, led Genesis’ $4.1 million seed round for a16z and co-led for a16z the company’s $200 million-plus Series B, with an undisclosed U.S.-based life-sciences-focused investor, and with Felicis Ventures serving as a major investor in that round..

潘德现在是安德森·霍洛维茨（AndreessenHorowitz，a16z）的普通合伙人，也是a16z生物基金的创始普通合伙人，他领导该公司跨生物学、计算机科学和工程的投资。潘德曾担任范伯格（Feinberg）的博士生导师，领导Genesis为a16z进行410万美元的种子轮投资，并与一名未披露的美国生命科学重点投资者共同领导a16z公司两亿多美元的B系列投资，Felicis Ventures是该轮投资的主要投资者。。

“I’ve had the really great fortune to be able to work with him for almost a decade at this point,” Feinberg said of Pande. “And I think it’s uncommon to be able to have to work so closely and both learn from and work with someone of that brilliance and vision.”

范伯格（Feinberg）在谈到潘德（Pande）时说：“我真的很幸运，能与他共事近十年。”。“我认为，能够如此紧密地合作，向如此才华横溢和富有远见的人学习并与之合作，这种情况并不常见。”

Evolving with the field

“He [Pande] has just constantly pushed me in a way that’s been really instrumental to the success that Genesis has had. And he continues to just constantly evolve as the field has evolved,” Feinberg added. “I think that’s parallel to our own maintenance of our status as a leader in the field, if that makes sense, and constantly innovating and not just being comfortable emulating, but instead actually pushing the field forward.”.

范伯格补充道：“他（潘德）一直在以一种真正有助于《创世纪》取得成功的方式推动我。随着这个领域的发展，他也在不断地发展。”。“如果这有道理的话，我认为这与我们自己保持在该领域的领导者地位是平行的，并且不断创新，不仅乐于模仿，而且实际上推动了该领域的发展。”。

During his graduate student days at Stanford, Feinberg recalled, AI primarily made its impact felt in computer vision and natural language.

范伯格回忆道，在斯坦福大学读研究生期间，人工智能主要在计算机视觉和自然语言方面产生影响。

“The neural network types that were used for both were actually quite distinct from each other, but neither were very applicable to chemistry. So, we developed new types of neural networks,” Feinberg recalled. “In the mid-2010s, it was graph neural networks that were better suited for molecules.”

范伯格回忆道：“用于两者的神经网络类型实际上是截然不同的，但两者都不适用于化学。因此，我们开发了新型的神经网络。”。“在20世纪10年代中期，图形神经网络更适合分子。”

Between then and now, Feinberg said, Genesis has consistently worked on new AI algorithms, “new neural network primitives that are better suited for the tasks of molecular AI.”

范伯格说，从那时到现在，Genesis一直在研究新的人工智能算法，“新的神经网络原语，更适合分子人工智能的任务。”

“Equivariant neural networks is one of those families that is important to us. And that is one of the areas that Nvidia is particularly helping us optimize,” Feinberg added.

范伯格补充道：“等变神经网络是对我们很重要的家族之一。这也是Nvidia特别帮助我们优化的领域之一。”。

Pande’s lab initially rose to prominence through his founding of Folding@Home, the distributed computing project designed to simulate protein dynamics, including the process of protein folding.

潘德的实验室最初是通过建立Folding@Home，该分布式计算项目旨在模拟蛋白质动力学，包括蛋白质折叠过程。

“Folding@Home used enormous numbers of Nvidia GPUs [graphics processing units] across the planet to do protein folding simulations,” Feinberg recalled: “Subsequent to that, Nvidia GPUs started to be used much more for artificial intelligence, specifically in vision and natural language. So, we as a company had already been, I would say, power users of Nvidia GPUs.”.

“”Folding@Home范伯格回忆道：“在全球范围内，我们使用了大量的Nvidia GPU（图形处理单元）进行蛋白质折叠模拟。此后，Nvidia GPU开始更多地用于人工智能，特别是视觉和自然语言。因此，我可以说，我们作为一家公司，已经成为Nvidia GPU的超级用户。”。

“Very natural fit”

“非常自然的贴合感”

“When we were introduced to Nvidia and NVentures through Series B, it felt like a very natural fit for an investor that would not only bring significant capital but also intellect as part of that relationship as well,” Feinberg said. “That investment then formed really the basis to have a relationship that grew beyond being a customer, but actually learning from each other as well, from our needs, and from their lower level capabilities that we could uniquely exploit given our domain knowledge.”.

范伯格说：“当我们通过B系列被介绍给Nvidia和NVentures时，对于一个投资者来说，这是一个非常自然的选择，不仅可以带来大量资金，而且还可以带来智力作为这种关系的一部分。”。“然后，这项投资真正奠定了建立关系的基础，这种关系不仅仅是作为客户，而且实际上也可以从彼此、我们的需求以及我们可以根据领域知识独特利用的较低级别的能力中学习。”。

For Nvidia, the collaboration with Genesis bolsters its ongoing efforts to apply AI toward drug discovery.

对于Nvidia来说，与Genesis的合作加强了其将人工智能应用于药物发现的持续努力。

“Genesis’ AI platform, and related computational advancements developed in collaboration with Nvidia, will help deliver novel generative and predictive AI techniques to explore untapped chemical pathways and identify drug candidates,” said Mohamed “Sid” Siddeek, corporate vice president at Nvidia and head of NVentures..

“Genesis的人工智能平台，以及与Nvidia合作开发的相关计算进步，将有助于提供新的生成和预测人工智能技术，以探索未开发的化学途径并确定候选药物，”Nvidia公司副总裁兼NVentures负责人Mohamed“Sid”Siddeek说。。

How will GEMS help Nvidia do both?

GEMS将如何帮助Nvidia做到这两个方面？

“The goal of GEMS is to be able to drug extremely challenging, in some cases, undruggable targets. And in order to do that, we need to accomplish several capabilities better than has existed before,” Feinberg said.

范伯格说：“GEMS的目标是能够对极具挑战性的，在某些情况下是不可药用的靶标进行药物治疗。为了做到这一点，我们需要比以前更好地完成一些功能。”。

Potency, selectivity, and atomy

效力，选择性和原子性

That includes generating molecules and predicting their potency, selectivity, and atomy characteristics—a joint, multi-parameter optimization approach to drug discovery for all key characteristics of a molecule together. GEMS consists of two deeply integrated pillars, Feinberg explained—generative AI and predictive AI—and has used Genesis’ own custom language models to generate anywhere from thousands to millions or billions of compounds in the cloud..

这包括生成分子并预测其效力，选择性和原子特性-一种针对分子所有关键特征的联合多参数优化药物发现方法。GEMS由两个深度集成的支柱组成，Feinberg解释了生成AI和预测AI，并使用Genesis自己的自定义语言模型在云中生成数千到数百万或数十亿的化合物。。

“But chemistry, synthetic chemistry is rate limiting. One can only make so many molecules in a given time. So it’s critical that our predictive AI technologies that predict potency, selectivity, and atomy are as accurate as possible. So, GEMS really is an umbrella that describes a deeply integrated set of technologies together,” Feinberg said..

“但是化学，合成化学是限速的。一个人在给定的时间内只能制造出这么多的分子。因此，至关重要的是，我们预测效力，选择性和原子性的预测人工智能技术尽可能准确。因此，GEMS真的是一把伞，它描述了一组深度集成的技术，”范伯格说。。

Using GEMS, Genesis is developing a pipeline focused on oncology and immunology. In oncology, Genesis is in late lead optimization phase, approaching the nomination of what it says will be highly potent and selective development candidates for pan-mutant allosteric inhibitors of PIK3CA, an oncogenic driver common to breast and colorectal cancers..

Genesis正在利用GEMS开发一条专注于肿瘤学和免疫学的管道。在肿瘤学方面，Genesis处于后期先导优化阶段，即将被提名为PIK3CA泛突变变构抑制剂的高效和选择性开发候选药物，PIK3CA是乳腺癌和结直肠癌常见的致癌驱动因素。。

Other oncology development efforts focus on small molecules intended to overcome response to checkpoint inhibitors (lead optimization phase) and prevent evasion of apoptosis in cancer cells by inhibiting an anti-apoptotic regulator of the extrinsic cell death pathway (discovery phase).

其他肿瘤学开发工作的重点是小分子，旨在克服对检查点抑制剂的反应（铅优化阶段），并通过抑制外源性细胞死亡途径的抗凋亡调节剂（发现阶段）来防止癌细胞凋亡的逃避。

In immunology, Genesis says it has two discovery-phase efforts—one to develop multiple programs for generating small molecules aimed at well-validated autoimmune disorder targets; the other, a treatment for “a severe, genetic autoinflammatory disease” using small molecule correctors to restore activity in an unspecified impaired protein..

在免疫学方面，Genesis表示，它有两个发现阶段的努力，一个是开发多种程序来产生针对经过充分验证的自身免疫性疾病靶标的小分子；另一种是治疗“严重的遗传性自身炎症性疾病”，使用小分子校正剂来恢复未指定受损蛋白质的活性。。

Collaborations with giants

In addition to its in-house development efforts, Genesis is pursuing collaborations announced with three biopharma giants, about which Feinberg said the company could not comment. The most recent was launched in September with Gilead Sciences, which agreed to discover and develop small molecule therapies across multiple targets, using GEMS to assist in generating and optimizing molecules for Gilead-selected targets..

除了内部的开发工作外，Genesis正在寻求与三家生物制药巨头的合作，Feinberg表示该公司无法对此发表评论。最近一次是在9月与吉利德科学公司（Gilead Sciences）合作推出的，该公司同意发现和开发跨越多个靶标的小分子疗法，使用GEM帮助生成和优化吉利德选定靶标的分子。。

Gilead agreed to pay $35 million across three targets and holds an option to nominate additional targets for an undisclosed predetermined per-target fee. Gilead also agreed to pay additional payments tied to achieving preclinical, development, regulatory, and commercial milestones, plus tiered royalties on net sales of commercialized products..

吉利德同意为三个目标支付3500万美元，并拥有提名额外目标的选择权，每个目标的预定费用未披露。吉利德还同意支付与实现临床前，开发，监管和商业里程碑相关的额外款项，以及商业化产品净销售额的分层版税。。

The other two collaborations with biopharma giants:

另外两项与生物制药巨头的合作：

• Eli Lilly—Up-to-$670 million partnership ($20 million of it upfront) to discover novel therapies for up to five targets across a range of therapeutic areas, initiated in 2022.

•礼来公司于2022年启动了高达6.7亿美元的合作伙伴关系（其中2000万美元是预付款），旨在为一系列治疗领域的多达五个目标发现新疗法。

• Genentech, a Member of the Roche Group—a multi-target, multi-disease effort launched in 2020, using Genesis’ platform for deep learning and molecular simulation. In 2022, Genentech described its targets of interest as “challenging targets that would elude other methods.” The value of the collaboration has not been disclosed..

。2022年，基因泰克（Genentech）将其感兴趣的目标描述为“其他方法无法实现的具有挑战性的目标”。合作的价值尚未披露。。

Genesis is headquartered in the San Francisco suburb of Burlingame, CA, with a fully integrated laboratory in San Diego. The company employs about 80 people.

Genesis总部位于加利福尼亚州旧金山郊区伯灵格姆，在圣地亚哥有一个完全集成的实验室。该公司约有80名员工。

“We do have a significant amount of expected growth, and that’s partially driven by both the Series B, this latest further investment from Nvidia, and our partnerships,” Feinberg said. “I don’t have a precise number where we’ll be in 12 months, but we do have substantial headcount to grow above that 80.”.

范伯格说：“我们确实有大量的预期增长，这部分是由B系列、Nvidia最新的进一步投资以及我们的合作伙伴关系推动的。”。“我没有一个确切的数字，我们在12个月内会达到什么程度，但我们确实有大量的员工人数来超过80人。”。

GEN EdgeNewsArtificial intelligenceCancer therapyComputational biologyImmunologyMachine learningMedicine, Diagnosis, and TherapeuticsTechnology and Computational BiologyTherapeuticsAndreessen HorowitzMerckMerck & CoMerck Research LaboratoriesNvidia

GEN Edgenews人工智能癌症治疗计算机生物学免疫机器学习医学，诊断和治疗技术以及计算生物学治疗学和Reessen-Horowitz-Merckmerck＆CoMerck研究实验室NVIDIA