Has the Inflection Point for AI for Science Arrived?

峰瑞资本·June 17, 2026

Where will the next Nobel Prize emerge?

AI for Science (hereinafter referred to as AI4S) is evolving from a niche, non-consensus track into a focal point where top global capital and leading pharmaceutical companies are placing concentrated bets.

FreeS Fund has been systematically building its presence in AI for Science. Following XtalPi's 2024 listing on the Hong Kong Stock Exchange as the "first AI drug discovery stock" — the company started out in crystal structure calculation — METiS Pharmaceuticals, which focuses on AI-enabled drug delivery, successfully went public on the Hong Kong Stock Exchange on May 13 this year, becoming the world's first AI drug delivery stock and the first AI large-molecule biopharma stock on the Hong Kong exchange.

Recently, at the Shanghai Caohejing Development Zone, Shanghai SDIC Pioneer Fund, FreeS Fund, and the Caohejing AI Accelerator co-hosted a thematic salon focused on "AI for Science (AI4S)" technological progress and industrial opportunities. Seasoned experts and investors from frontier fields including biomedicine, new materials, and cell engineering engaged in an in-depth discussion on "how AI is rewriting the paradigm of scientific research."

Following five thematic presentations, the roundtable was moderated by He Miao, Deputy General Manager of Shanghai SDIC Pioneer Fund. On stage were three scientist-CEOs from FreeS Fund's portfolio: Zhang Wenbin of Tuoyin Technology, Liu Tiangang of Hesheng Technology, and Li Xin of Baiyao Technology, alongside Rui Ma, Partner at FreeS Fund.

Group photo of the roundtable guests. Photo source: on-site capture.

The roundtable covered these topics:

AI drug discovery can actually produce drugs now, but where exactly is the next inflection point? Could AI virtual cells be the next AlphaFold moment?
For scientists turned CEOs, the hardest part isn't the technology — it's "the ability to let go"?
How do you align the digital world with the physical world? How do you recruit people who can outperform the CEO?
On a long-cycle, high-barrier track like AI4S, what kind of capital and what kind of post-investment support do entrepreneurs actually want?
Where will the next Nobel Prize land first — a blockbuster natural molecule, or an AI stronger than Einstein?

Below is the edited transcript of the conversation.

Interactive Giveaway

What do you think about the future of AI4S? Share your thoughts in the comments. By 17:00 on June 26, 2026, the two most thoughtful commenters will each receive a book recommendation from Feng Shu (Li Feng).

01 What exactly is the inflection point for AI4S?

He Miao: Before we dive in, please briefly introduce yourselves.

Zhang Wenbin: I trained in organic chemistry and did my PhD in polymer science at the University of Akron. I then did postdoctoral work in the labs of David Tirrell and Frances Arnold at Caltech, where I started working on biological macromolecules. I returned to Peking University in 2013 to work on topological proteins, and later founded Tuoyin Technology with my team to commercialize this work.

Liu Tiangang: Hesheng Technology focuses on natural products — the flavors you taste, the floral scents you smell, the colors you see, the functional ingredients in personal care products, animal nutrition and crop protection in agriculture, and small-molecule natural drugs all fall within this category. The company grew out of technology transfer from Wuhan University.

Li Xin: I'm from Baiyao Technology and also affiliated with the Institute of Zoology at the Chinese Academy of Sciences and the Beijing Institute for Stem Cell and Regeneration. Before returning to China, I was at MIT. My research spans stem cells and developmental regenerative medicine, epigenetics, aging, and disease — it looks broad, but the underlying core question is consistent: the middle layer between life's central dogma, the information encoded in sequences, and the resulting macroscopic phenotypes.

Rui Ma: I'm Rui Ma from FreeS Fund. I do a lot of early-stage investing in interdisciplinary fields. AI drug discovery and AI for science are both key focus areas for us, and all three entrepreneurs on stage today are from our portfolio. I'll also share our firm's views on the AI4S sector in a moment.

He Miao: The fact that we're all sitting here already shows we're bullish on AI4S. So my first question: what was the moment that brought you into contact with AI, how has it impacted your R&D, and do you believe we've now reached the golden moment for AI4S commercialization?

Zhang Wenbin: I used to work on synthetic polymers — an extremely imprecise structural system. Switching to proteins felt like leaping from the Stone Age into the Information Age. The sequence is determined, and after folding, every atom's position is determined too, yet the evolutionary possibilities are virtually limitless.

When we first started designing topological proteins, my students and I would sit in the office for over ten hours at a stretch, wrestling with wire models. But after 2018, as structure prediction, language models, and generative models emerged one after another, the entire workflow suddenly became much more controllable — to the point where even undergraduates in the lab can pick it up quickly now. So is AI4S the future? Without question, and the second half will move very fast.

It's just that we're not taking the usual path. Proteins in nature are all linear structures because the ribosome's template-driven polymerization can only produce linear chains — but this isn't a necessary constraint. If we open up the backbone into rings, knots, lassos, or links, it's like opening an entirely new "sequence universe" for proteins. Nature has evolved for billions of years, yet there are only about 1,300 naturally occurring protein backbone topologies. We've directly expanded that number to the 10⁴ level, and with generative models we can push it further to 10⁵. The significance isn't in the quantity — it's that we can use more stable, patent-bypassing new scaffolds to essentially "rebuild" the same function from scratch. This is the core we'll expand on later.

Liu Tiangang: Scientific and industrial progress is often driven by lazy people. Diligent people will stay up for days to finish experiments, but there are always those who don't want to work that hard and start looking for easier ways.

He Miao (laughing): Professor Liu, are you the lazy one?

Liu Tiangang: I wouldn't put it that way. But AI lets us extract patterns that used to be accumulated through experience, replacing the old high-throughput experimental approach. Once you see that, you actively embrace it.

For our industry, the significance of the inflection point is even greater. Humanity spent over a century identifying 400,000 natural products from nature, of which 4,000 are currently in use — a 1% success rate. But we've only studied 10% of Earth's species; the potential natural products could number 4 million, or even 40 million. Finding them used to require going to the ends of the earth. Japanese microbiologist Satoshi Ōmura brought back a handful of soil from a golf course and isolated avermectin from it, which won him the 2015 Nobel Prize in Physiology or Medicine. Pfizer's first patented compound, terramycin, discovered in 1949, came from the same methodology. But this paradigm has reached its end. Big pharma has cut their natural products departments. It's not that there's nothing left — it's that the old methods can't find them anymore.

So we changed paradigms: using deep learning to predict silent genes from genomic data, then high-throughput gene synthesis to have industrial chassis cells "remotely extract" the molecules. This may be the last land rush on Earth.

Li Xin: I work in cell biology and stem cells. When iPS (induced pluripotent stem cells) emerged in 2006, there was a wave of excitement about using stem cells for regenerative medicine, but that promise has never been fulfilled. Stem cell pipeline development, differentiation, and optimization are all too slow, relying on manual experience and various inducers through trial and error.

Around 2010, people started using gene regulatory networks to predict cell states, but the ceiling was low. It wasn't until 2018, when AlphaFold came out, that we realized deep neural networks have very strong fitting capabilities for complex systems. We started wondering: could we use AI to directly simulate continuous changes in cell states? But at the time, neither data volume nor algorithms were sufficient. By 2022, we had figured out how to do this with self-supervised training. The moment we got it working was exhilarating — then we realized we weren't the only smart people in the world; three teams were working on this simultaneously. In academia, this later became known as "AI virtual cells."

2023 was year zero for virtual cells. That year, academia for the first time combined large-scale single-cell resolution transcriptomic data with Transformer self-supervised architectures, producing four models: Geneformer, scGPT, scFoundation, and our team's GeneCompass. By December 2024, "AI virtual cell" was formally proposed as a term; 2025 has seen dual explosion in industry and academia. We've accumulated nearly 600 million single-cell data points, covering virtually all human cell types. Our model placed second globally and first domestically in the virtual cell challenge. The cell is the next AlphaFold moment we're waiting for.

We're deeply convicted about this. It's a foundational revolution, qualitatively different in capability and imagination from past work at the single-molecule level.

Rui Ma: About ten years ago, FreeS Fund invested in both XtalPi and Bluepha — one in AI drug discovery, one in synthetic biology. From then on, we've been accustomed to looking at biology through an AI lens. But to be honest, AI drug discovery was criticized for four or five years — everyone felt it could only solve marginal efficiency gains, and only in one particular环节, and more critically, that it hadn't actually produced any drugs.

We're optimistic because we've witnessed firsthand how technology has iterated and ascended step by step. And through these companies' development, we discovered that what really matters isn't just the technology — it's the founders' faith in AI. They jumped in when everyone was still questioning, and were rewarded by capital and markets. XtalPi is the archetypal example. Of course, METiS Pharmaceuticals is too.

Today we see at least five signals of inflection.

First, multiple AI drug discovery companies have reached IPO — XtalPi, Insilico Medicine, and METiS are all listed in Hong Kong, commonly known as the "three little dragons of AI drug discovery."

Second, more and more drugs are actually being made by AI.

Third, a new generation of models has emerged in large numbers over the past two to three years — AlphaFold 3, RFdiffusion, ChAI. Past achievements were based on older, earlier models; the new models open up vastly greater imaginative space.

Fourth, multinational pharmaceutical companies have started making major investments, binding themselves to model companies. Eli Lilly gave ChAI tens of millions of dollars, opening up 150 years of their data.

Fifth, the paradigm for data collection has changed — from manual experiments to agents plus automated high-throughput. In short, AI drug discovery has evolved from a research tool to producing real drugs, from concept to cash flow.

Based on these, I'm extremely optimistic about AI4S over the next five years.

He Miao: It's clear that FreeS Fund is an institution with conviction in AI4S. I'd like to push further: among all the frontier industries — AI + materials, quantum mechanics, controlled nuclear fusion, and so on — where will the first true commercial closed loop emerge?

Rui Ma: My judgment is that it will happen first in fields that already had decent data foundations and have been experimenting with AI. Such as bio-manufacturing, brain-computer interfaces, and AI virtual cells. Actually, the question you're asking is essentially: where will new data come from going forward. Wherever new data can grow stably, there is potential for grounded applications.

These three directions actually share the same underlying framework. AI's own progress doesn't directly translate to productivity. Real productivity comes from AI's penetration into various industries. AI first diffuses into physics, chemistry, materials, biology — completing 0-to-1 scientific innovation within them; these innovations then combine with industrial demands to complete 1-to-100 scale-up.

Bio-manufacturing, brain science, and AI virtual cells will be the next few closed loops because they're already positioned midway along this chain. A US company used generative AI to improve battery watt-hours per kilogram by 50% in two weeks — a metric that had progressed less than 1% annually over the past 150 years. In brain science, a company we invested in, NeuroXess, has achieved excellent efficacy in treatment-resistant depression and Parkinson's disease, and has identified new targets. In nuclear fusion, plasma prediction, control, and design are virtually impossible without AI; future reactor design will also require AI.

Based on these, I'm extremely optimistic about AI4S over the next five years.

02 What is the biggest challenge in entrepreneurship?

He Miao: My next question is for the three professors. All of you have done substantial work not just in research but also in industrialization. Scientists inevitably face many challenges in the entrepreneurial process — technology transfer, team building, business models. Could you share your experiences?

Li Xin: I think the biggest challenge is the shift in identity. Before leaving academia, we tended to think from a technical perspective, believing that capability boundaries were what mattered most. But entering industry, you realize it's a more comprehensive endeavor. You need the technology itself, but also market feedback — and the market includes both capital providers and customers. My biggest takeaway from the entire process is that what it really tests is your ability to learn quickly. Every aspect is new, and you have to try them all. So this is the fundamental challenge.

Liu Tiangang: I've had quite a bit of experience interfacing with business. During my postdoc, I was in the US during the earliest wave of synthetic biology's rise, watching these companies emerge and decline. After returning to China, I invested in some startups, so I'd seen quite a lot. Looking back, I now think the biggest challenge is the ability to let go — the ability to subtract.

Identity transformation, team building, sales — professors can do all of these well if they want to; they just didn't know how before. But what's truly difficult is something else. I used to never refuse anyone who came to me for help; I was very capable and could get things done. But was all that effort worth it? At this stage, the biggest challenge is finding a higher-growth track.

Take several of our current products as examples. In the myocardial infarction space, we've developed a compound codenamed HS-88. Colchicine has long been a potential candidate for MI treatment, but its safety window is too narrow. Our compound HS-88, even before structural modification, is much safer than colchicine while being more effective. In mosquito repellency, we've produced artemisia alcohol, effective against mosquitoes and ticks at 50 ppm concentration — it worked even in Xinjiang's Beiwan, where there are 11,700 mosquitoes per cubic meter. In aroma molecules, we've made nootkatone, using large models to predict molecular flavors and then synthesizing derivatives to create next-generation perfume replacement molecules. These three products serve three completely different customer groups: pharmaceuticals, agrochemicals, and personal care.

Zhang Wenbin: Product selection is indeed extremely challenging — it requires not just technical understanding but also market and business model acumen. I strongly agree with what Professor Liu just said: you must decide not just what to do, but what not to do.

When we first established our technology platform, I was ambitious, feeling that proteins as the executors of life functions could do virtually anything — drugs, materials, catalysts. But when it came to actual implementation, reality tells you there are strong competitors on every path. Where exactly is the product that best fits you and lets you leverage your strengths? At this point, the most brutal yet most important demand on founders is upgrading your "academic taste" to dual sensitivity for both science and business. This is a massive challenge.

Facing this challenge, our solution was to clearly divide our product pipeline into short, medium, and long-term stages. The first to achieve commercial closed loop was "enzymes" — from fine chemicals and pharmaceutical intermediates to PET plastic-degrading enzymes — all now with repeat customers. The second layer is large-molecule drugs, the market with the greatest imaginative space; we're taking existing protein drugs and making topological versions to improve stability and reduce immunogenicity. The third layer is complex soft matter, such as dye-based liquid crystals — our most long-term direction, more of a "training ground" to test our AI's generalization capabilities.

/ 03 /

What threshold must be crossed next?

He Miao: How do you allocate your time and energy across science, commercialization, AI, and so many cross-disciplinary fields? And in your respective directions, what are the key technical breakthroughs needed next? Is accumulating more data and closing the wet-dry experimental loop sufficient, or are there other technical hurdles?

Zhang Wenbin: Time balance is indeed very challenging for me. I'm someone with intense curiosity, particularly greedy for learning. But this creates enormous pressure — there's never enough time.

In the end, I found the solution has to come back to strategy: just as company operations need strategy, personal development equally requires trade-offs. A person's scarcest asset is often not wealth, but attention and focus. So I'm now working hard to laser-focus my energy on the most core priorities, pushing everything else forward through full delegation and teamwork. In scientific research, one person with one outstanding strength can punch through a single point; but in business, no matter how long your long board is, it can't beat a system. This is inevitably a team victory.

On the next breakthrough: I believe the core bottleneck for AI for science落地 lies in accelerating the alignment and integration between the digital virtual world and the physical real world. Our TopoEvolver platform is designed precisely to address this enormous challenge. It's different from traditional high-throughput automated experiments. Its "throughput" is flexible, elastic, and intelligent. It can autonomously read literature like a scientist, execute different experimental workflows, and even independently judge at what point in time to combine which AI models to tackle a specific problem.

He Miao: Let me push further. You mentioned in your presentation that protein structure prediction is already a red ocean — so compared to traditional protein prediction, what incremental gains does your topological design actually provide?

Zhang Wenbin: Traditional protein structure prediction tends toward understanding "what a protein is." Our topological technology, by contrast, rewrites the backbone from the ground up. The same function can run on a more stable new scaffold. This is a system entirely focused on enhancing function.

Traditional linear proteins often face an enormous conformational space, but our topological structures can reshape this space, "framing" it near the functional conformation. When the conformational space is concentrated, function naturally improves, and so does evolvability.

In other words, the previous wave of structure prediction could be called the "first half" of protein research; the second half will definitely be about function — who can control it and how to enhance it. We and traditional linear proteins are on completely different paths. They're making internal variations on nature's existing scaffolds, competing with each other; we've entirely jumped out to open a new track, with infinite possibilities because topological types are essentially infinite.

Liu Tiangang: In time allocation, everyone has a focus at any given period — you can't truly advance on all fronts simultaneously. The hardest next step for us is organizational building. In the early stage, we relied on scientific and technological advancement plus personal involvement to carve open market opportunities. But to replicate from 1 to 100, you can't rely solely on the original team — you need subsequent organizational structure, and the ability to attract and cultivate people who can outperform you.

Li Xin: Our field is very new — AI virtual cells have only existed as a term for two years. Before that, computational biology used partial differential equations for cell simulation, but very few people use today's AI algorithms for simulation. So I also spend the most time on team building, especially cross-disciplinary teams. How do AI-native talents, originally from autonomous driving or general computer vision, match with life science backgrounds, especially cell biology? And that's not all — you also need to find application scenarios, and going from drug development to clinical application is another very long process.

When external talent is insufficient, you cultivate internally. We have a mechanism where people are mandatorily brought together in a physical space for brainstorming, melding the most cutting-edge academic techniques with industrial demands.

The biggest bottleneck for the entire field is still exploring the optimal applications for AI for science. The technology is so new that currently, the drug development path has many segments and many possible directions, but energy is limited, so we're also doing subtraction.

/ 04 /

What kind of money do entrepreneurs want?

He Miao: As an investment platform representing the Shanghai municipal government, we'd also like to talk with Mr. Ma and the scientists. Shanghai, including state-owned capital platforms, now treats AI for science as a strategic priority. So what are your expectations for the government side, the investment institution side? In terms of infrastructure, environment building, talent recruitment — over the past two years we've done quite a bit in computing subsidies and special talent recruitment policies. Specifically for the AI for science direction, we'd like to hear your thoughts on what we can do going forward.

Rui Ma: First, more money is needed to support AI for science entrepreneurs — this very much needs SDIC's support. Second, AI for science already has consensus in Shanghai, but overall it's still a non-consensus track. After money comes in, how to allocate it toward non-consensus directions is crucial.

Liu Tiangang: First, there needs to be serious money. Our company deals directly with large foreign companies, so we feel this very directly. The real competition is between China and the US. Compared to the US, most original models come out there first; we're doing follow-on innovation. In this landscape, there are now some excellent seed-stage companies on tracks that others haven't started yet, doing meaningful work. They should be resolutely invested in more heavily, to build these seed players into star companies.

Li Xin: First is resource integration. Money is one aspect, but more important is the upstream-downstream对接 ecosystem chain — this is where government guidance can play to its strengths. Second, around biopharmaceuticals. The key for AI for science is end-to-end: can your final product, your final output, ultimately reach populations, reach patients? How to string together final results across such a long chain requires coordination with front-end resource integration. So downstream market cultivation — such as drug reimbursement policies, acceleration policies during drug development — are also directions to pursue together.

Zhang Wenbin: AI for science currently faces quite a large industrial delivery problem. But I believe this difficulty is often not really due to inadequate technology, but to poor problem selection. Before product initiation and launch, people may not have communicated sufficiently with industry, or the team is entirely scientists without someone with industrial sense and business understanding. The result is a lack of reliable assessment of real costs and whether a problem is truly a hard need. So if there could be more "personalized" coaching in this area, more connections to industrial resources — especially bridging overseas industrial circles and resources — plus help with talent recruitment, that would be enormously helpful to us.

/ 05 /

Where will the next Nobel Prize appear?

He Miao: Shanghai is promoting AI for science to drive the evolution of the entire research paradigm. The 2024 Nobel Prize went to the AlphaFold team. We hope this wave of cultivation can produce new Nobel laureates in China. So please make bold predictions: in what field might the next Nobel Prize emerge?

Liu Tiangang: Let's look at history. China's only Nobel Prize in natural sciences went to artemisinin, a small molecule. Tu Youyou's name itself comes from the Book of Songs line "Youyou deer cry, eating the wild artemisia." Nobel Prizes generally fall into two categories: major technical or model breakthroughs, like gene editing or AlphaFold; and truly problem-solving drugs or molecules, like artemisinin. You need a molecule with massive industrial scale and desperate unmet need to reach that level. So probabilistically speaking, if we can rapidly find a blockbuster from what nature has evolved, there might be a chance.

Zhang Wenbin: From a fundamental principles perspective, it seems the major directions were largely "exhausted" by the last century. Today's scientific breakthroughs more often grow from these most底层 principles into discoveries with particularly large impact, solving certain long-standing specific problems. As Professor Liu just said, it might be an epoch-making drug, or it might be a disruptive technology. Nuclear fusion is one such possibility; a new drug is another. The greatest value AI can provide here is dramatically accelerating the speed at which these possibilities "surface." It will break原有的 R&D cycles, making emergences that were previously out of reach into realities we can witness with our own eyes.

Li Xin: The answer varies depending on granularity. Life sciences cover a very broad surface among important natural science questions, from macro to micro with many layers, and high-impact achievements are possible at every scale. So there will be relatively more Nobel points in this field. In other disciplines, quantum directions are also possible. What AI for science can truly do is accelerate our ability to see hidden corners that are currently unseen and un-inferred. Exactly which point will explode is very hard to predict in advance. Life itself is a complex network; every level, dimension, and scale is possible. Our work on virtual cells is essentially trying to string these hierarchies together, finding the truly important targets from a systems perspective.

He Miao: The virtual cell field is a grand integrator of technologies. Progress at the molecular and protein levels may still be needed first.

Rui Ma: We're still proving ourselves every day — that we can make drugs, produce molecules, use AI virtual cells to make specific predictions — because the field as a whole remains non-consensus. But if I were to make a bold prediction, I don't think AI can reach superintelligence without AI for science. There must be an intermediate stage where AI for science surpasses human scientists at a certain level. DeepMind founder Demis Hassabis has said they want to build an AI stronger than Einstein. That might also be a Nobel direction. In the future, AI for science may have a broader concept. This path differs from our daily emphasis on "steadily making molecules," but it's worth looking forward to.