Why can the most "hardcore" Chinese female CEO in the Silicon Valley AI circle make pharmaceutical giants line up for cooperation?
Do you still remember the "Ice Bucket Challenge" that swept the globe in the summer of 2014?
When business leaders, movie stars, and ordinary people poured buckets of ice water over their heads and felt the instant "freeze," this phenomenon-level online carnival also brought a cruel disease - Amyotrophic Lateral Sclerosis (ALS), which we commonly call "Lou Gehrig's disease," into the public eye on such a large scale for the first time.
This is a real medical "incurable disease." Patients' brains gradually lose control over their muscles, while they watch their bodies being "frozen" inch by inch with a completely clear mind, and eventually succumb to respiratory failure.
For more than a century, ALS has always been the "valley of death" in the field of new drug research and development - the failure rate of research and development exceeds 90%. It often takes more than a decade and billions of dollars to bring a new drug to market. Even the world's top pharmaceutical giants have repeatedly failed and been at a loss in this area.
However, in this field that has almost been pronounced "unsolvable," a young Chinese female founder - Alice Zhang, and her company Verge Genomics, are trying to use a brand-new weapon - AI + human genomic data - to directly tackle this "impossible" task.
What exactly is this AI upstart, which has Chinese roots and was born in Silicon Valley, doing? How are they faring in the face of this century-old problem? And what are their advantages compared with local biotech giants?
Today, let's embark on the story of Verge Genomics and explore the answers to these questions.
01
In the face of an "incurable disease" like ALS, traditional methods have clearly reached a dead end. To break the deadlock, a completely new approach is needed. Verge Genomics' answer is both bold and rational.
Core concept: "All-in-Human," completely bid farewell to mice
First, let's face a soul-searching question that has puzzled scientists in the field of drug research and development for decades: Why do drugs that show significant effects in mice often fail in human clinical trials?
This is the famous "valley of death" in research and development. Countless promising candidate drugs have failed here, costing pharmaceutical companies billions of dollars in investment and more than a decade of precious time, and dousing patients' hopes time and time again.
The root of the problem lies in the over-reliance on animal models.
Alice Zhang, the founder of Verge Genomics, and her team believe that there are huge genetic differences between humans and mice. Using a wrong model to solve a correct problem means starting off on the wrong foot.
Therefore, they put forward a highly subversive core concept - "All-in-Human" (driven by all human data).
Simply put, it means completely skipping the intermediate step of animal experiments and directly letting artificial intelligence learn about real-world human diseases from the very beginning.
To realize this vision, they dedicatedly built the company's core technology engine - the CONVERGE® platform.
This platform only does one thing: Extract multi-dimensional biological data, including genomic and transcriptomic data, from a vast amount of real human brain and spinal cord tissue samples from patients (including deceased donors), and then "feed" this data to a powerful AI.
It's like, to cure a person's disease, instead of letting AI read a translated and error-ridden "Mouse Disease Guide," we directly hand it a high-definition original "Human Disease Manual."
By learning from the data of a vast number of real patients, AI can identify gene targets truly related to the disease with unprecedented precision and predict which drugs can act on these targets. This fundamentally improves the success rate and efficiency of drug research and development.
And the person who came up with this subversive idea is not a senior pharmaceutical expert with gray hair, but a young Chinese female founder - Alice Zhang.
Her resume is a model for academic achievers: She graduated from Princeton University with a major in molecular biology, and then entered the top-ranked MD/PhD (Doctor of Medicine/Doctor of Philosophy) joint program at UCLA-Caltech in the United States.
Following this glittering academic path, she could have become an excellent doctor or scientist. However, during this period, she keenly noticed a huge "gap": on one side was the rapidly developing AI and big data technology, and on the other side was the traditional biological laboratory with slow progress and high barriers. There seemed to be an invisible wall between them.
She was not content to just publish papers in the ivory tower. She was more eager to "tear down that wall" with her own hands and use the most cutting-edge technology to solve the most difficult medical problems.
Therefore, she made a brave decision: Leave the doctoral program at a top university and founded Verge Genomics in 2015.
It can be said that Alice Zhang's "cross - border" genes were injected into Verge's DNA from the very beginning. She understands cutting - edge biological science and firmly embraces AI and big data. It is this unique perspective that enables her and her team to break the rules and use a brand - new logic to challenge the seemingly insurmountable mountain of "Lou Gehrig's disease."
02
In Silicon Valley, a subversive idea may help you secure financing, but only hard - core achievements can earn you real respect at the table full of giants. This is especially true for Verge Genomics, which is in the "life - or - death" field of new drug research and development.
So, how is this Chinese - founded AI company faring? The answer lies in its research and development pipeline and its "circle of friends."
Milestone pipeline: From 0 to clinical trials in just 4 years
The most core measure of the value of a biotech company is the progress of its drug research and development pipeline. On this report card, Verge's performance is amazing.
Verge's leading project is a candidate drug called VRG50635, which targets the century - old problem we mentioned at the beginning - "Lou Gehrig's disease" (ALS).
What is most shocking is its speed: from the moment AI locked in a new drug target in a vast amount of human genetic data to developing it into a candidate drug and sending it into human clinical trials, Verge only took a mere 4 years.
What does this mean? In the traditional pharmaceutical industry, it takes an average of more than 12 years to go through the same process. Verge has shortened this cycle by two - thirds. This is not only a victory in terms of time but also an ultimate verification of the predictive ability and efficiency of its AI platform, CONVERGE®. Currently, VRG50635 has successfully entered the clinical trial stage, where its safety and effectiveness in real patients are being verified, bringing new hope to millions of patients around the world.
More importantly, Verge's platform is not a one - time success. They quickly replicated this "AI - based drug discovery" ability from the "tough nut" of neuroscience to another globally - watched blue - ocean market - obesity and metabolic diseases.
Its second candidate drug, VRG201, was developed for this purpose and is currently in the final sprint stage before clinical trials (IND - enabling). This fully demonstrates that Verge's technology platform has strong scalability. It is not a "specialist" that can only solve a single problem but a "generalist" that can empower multiple disease areas.
If the progress speed of its self - developed pipeline shows Verge's "internal strength," then its star - studded "circle of friends" represents the trust votes cast with "real money" by the top players in the entire industry.
These pharmaceutical giants have the world's top scientific teams and the strictest evaluation criteria. When they decide to extend an olive branch to a startup, it means that the company's technology has passed the most rigorous tests.
Joining hands with Eli Lilly: In 2021, Verge reached a cooperation agreement with the global pharmaceutical giant Eli Lilly to jointly develop new therapies for "Lou Gehrig's disease." This cooperation not only brought upfront funds to Verge, but the subsequent milestone payments and potential sales royalties could be as high as hundreds of millions of dollars. This is equivalent to Eli Lilly admitting that Verge's AI platform can find treasures that even they have difficulty discovering.
Partnering with AstraZeneca: After Eli Lilly, another global top - tier pharmaceutical company, AstraZeneca (through its subsidiary Alexion), also extended an olive branch to Verge. The two parties reached a cooperation agreement to use Verge's AI platform to find new targets for rare neurodegenerative diseases and neuromuscular diseases.
The significance of these top - level partnerships goes far beyond the funds themselves. It is the strongest endorsement, announcing to the world that Verge Genomics not only has an exciting story but also a powerful engine that has been verified by industry giants and can continuously produce high - value results.
03
In Silicon Valley, Verge Genomics is not alone. The AI - powered pharmaceutical track is already full of competitors, with many star companies emerging, such as Insitro founded by Stanford's "AI goddess" Daphne Koller, and Recursion Pharmaceuticals, which has successfully gone public. Like Verge, they all wave the AI flag and are committed to subverting the traditional long and expensive drug research and development model.
So, in the face of competition, what are the unique features of Chinese - founded AI companies represented by Verge Genomics? And how does it build its own moat?
1. Strategy comparison
The common point among all AI - powered pharmaceutical companies is the recognition of the inefficiency of the traditional research and development model. However, the paths to solving this problem vary.
The common path in the United States: Taking Insitro as an example, it conducts large - scale, high - throughput experiments on human induced pluripotent stem cells (iPSC) in the laboratory to generate a vast amount of data for AI model training. This is undoubtedly a step forward as it gets rid of animal models, but the data essentially still comes from an "in - vitro" experimental environment.
Verge's differential advantage: Verge's "all - in - human" strategy is more extreme and decisive. It chooses to directly jump to the source closest to the end - real human patient tissue samples. By analyzing a vast amount of human brain and spinal cord tissue data, Verge's AI learns from the very first day the complete biological story of the disease that has occurred in the complex environment of the real human body.
This is not just a difference in data sources but a philosophical difference that is infinitely close to the clinical reality from the very beginning. It bets that by directly inferring from the "end - game," it can bypass the biggest trap in drug research and development - the "Translational Gap," that is, the problem that laboratory results cannot be replicated in real patients.
2. Closed - loop comparison
The predictive ability of an AI model needs to be verified and iterated through real biological experiments. How to build this "computation - experiment" closed - loop is the core key for all companies.
The cooperation tendency in the United States: Some AI companies tend to play the role of a "platform." That is, after using AI to find potential targets, they license or sell them to large pharmaceutical companies for subsequent development. This model is relatively light, but the feedback loop is long, and the iteration speed of the AI model will be restricted by external cooperation.
Verge's differential advantage: From its inception, Verge has been committed to building a powerful internal closed - loop from "AI target discovery" to "internal medicinal chemistry" and then to "early - stage clinical development." This means that once the CONVERGE® platform predicts a new target, Verge's internal scientists can immediately conduct verification and drug design in their own laboratories. The advantages of this model are obvious:
Ultra - fast iteration: The results of successful or failed experiments can be quickly fed back to the computing team to optimize the AI model, forming a rapidly rotating "learning flywheel."
Knowledge internalization: All the valuable experience and data in the research and development process are accumulated within the company, becoming the continuously strengthened core assets.
Reduced friction: It avoids the delays and losses caused by communication, priority, and intellectual property issues when cooperating with external partners.
Verge's success marks an important shift. In the past, the entrepreneurial stories of Chinese in Silicon Valley were more concentrated on application software or business model innovation. Verge proves that the Chinese community is fully capable of making world - class original breakthroughs in the "hard - core" field of AI + biotech, which requires extremely high technological barriers, profound scientific knowledge, and long - term vision.
Conclusion
Of course, Verge's journey is far from over. The clinical data of its first drug, VRG50635, will be the ultimate "test" to examine the quality of its platform. However, this is not only a test for a single company but also an excellent window to observe the times.
Through Verge, we can see the infinite possibilities of the combination of AI and life science, and also witness a new wave of Chinese entrepreneurs reshaping the global frontier technology landscape with unprecedented depth and breadth. Their stories are worthy of our continuous attention and anticipation.
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