Which AI drug discovery startup is growing the fastest?
In our AI in drug discovery market deck, you will find everything you need to understand the market
SUMMARY
Chai Discovery is the fastest-growing independent AI drug discovery startup today, because customer adoption, deployment depth, financing and technical momentum are all accelerating at the same time.
The key point is that Chai is not winning on one giant number. Its $3.8 billion valuation matters, but the stronger evidence is that major pharma companies kept adopting the platform as the valuation rose.
Chai is also benefiting from a software-like growth model. It can work across multiple pharma discovery programs in parallel, while a biotech advancing its own drug has to move sequentially through preclinical and clinical development.
The pharma adoption looks more substantial than a collection of AI pilots. Lilly, Pfizer, Novartis, argenx and Bristol Myers Squibb have tied Chai to proprietary data, custom models, antibody programs or wider discovery workflows.
Chai-2 appears to have been the technical inflection point. Its unusually high experimental hit rates from very small design batches were followed by a surge in pharma interest, making the commercial reaction hard to dismiss as pure hype.
Chai-3 may be better still, but the public evidence is less mature. Pharma behavior gives the model credibility, yet its strongest performance claims still rely more heavily on company data than the Chai-2 results do.
Iambic is ahead on the toughest form of validation: an AI-designed drug has already reached patients and shown early clinical activity. If the question were which startup is proving fastest that AI can produce real medicines, Iambic would win.
Genesis Molecular AI leads on disclosed near-term pharma economics. Its expanded Incyte deal includes $80 million of non-refundable upfront cash plus a $40 million equity investment, giving us a much clearer view of actual money changing hands than most headline AI-biotech partnerships provide.
Isomorphic Labs is bigger by capital, but its Alphabet ownership and DeepMind origins make a direct startup comparison awkward. Its $2.1 billion Series B says a lot about scale, though less about how fast an independent company can build from scratch.
Billion-dollar partnership headlines are a poor shortcut for growth. Most of those figures are milestone ceilings that may take years to earn, so repeat adoption, upfront payments and wider deployment tell us more about the business today.
Chai’s valuation has probably run ahead of anything we can directly measure in revenue, so “fastest-growing” should not be confused with “cheap.” Insilico Medicine, now public, is the useful reality check: it already combines triple-digit reported revenue growth, profitability and a Phase 3 program.
This market map, featured in our AI in drug discovery market deck, highlights top companies and startups in the AI in drug discovery market
Which AI drug discovery startup is growing the fastest?
Why is the fastest-growing AI drug discovery startup so hard to identify right now?
The fastest-growing AI drug discovery startup depends on what we call growth, because Chai Discovery, Iambic Therapeutics, Genesis Molecular AI and Isomorphic Labs are currently winning very different races.
A software-like drug design platform can add pharmaceutical customers in a few months. A biotech developing its own medicines may need years to move from molecule design to human trials. A third company can announce a multibillion-dollar pharmaceutical partnership even though only a fraction of that money is guaranteed.
Those differences are unusually large in AI drug discovery.
Chai's growth is easiest to see in pharmaceutical adoption and valuation. Iambic has made more progress getting an internally discovered drug into patients. Genesis has recently shown stronger disclosed upfront economics from a pharma partnership. Isomorphic has raised vastly more capital, although its position inside Alphabet makes it a strange comparison with an independent startup.
For this article, we therefore treat "growing fastest" as the rate at which an independent private company is expanding its real position across several dimensions at once: customers, deployment, financing, technology and drug-development relevance. Raising one giant round or announcing one giant milestone package cannot decide the ranking by itself.
Using that definition, Chai Discovery currently has the strongest case.
Which companies should actually count as AI drug discovery startups?
The most useful AI drug discovery startup comparison today is between independent private companies where AI is central to finding or designing medicines.
That immediately creates two awkward cases.
Insilico Medicine would otherwise be one of the strongest contenders in the entire article. It now has reported revenue, profit and a Phase 3 program, but it is listed in Hong Kong. We will use Insilico later as a benchmark rather than call it a startup.
Isomorphic Labs sits on the other side of the boundary. It operates with its own CEO, scientists, partnerships and outside financing, yet it was created from DeepMind and remains an Alphabet company. It has resources and institutional backing that Chai, Iambic, Genesis and Profluent did not have when they started.
We still need Isomorphic in the comparison because ignoring one of the biggest AI drug design operations in the world would distort the analysis. We just should not let a $2.1 billion financing round automatically settle a startup race.
The core group we examined most closely is therefore Chai Discovery, Iambic Therapeutics, Genesis Molecular AI, Profluent and Xaira Therapeutics, with Isomorphic Labs and Insilico Medicine used as important benchmarks.
As this chart shows, and as featured in our AI in drug discovery market deck, search interest in AI drug discovery has grown rapidly
Is Chai Discovery really the fastest-growing AI drug discovery startup right now?
Yes. Chai Discovery currently looks like the fastest-growing independent AI drug discovery startup when we combine customer adoption, financing and the speed at which its technology is moving into pharmaceutical workflows.
Chai was founded in 2024. It has already raised roughly $630 million, reached a $3.8 billion valuation and turned major pharmaceutical companies into users of its molecular design technology.
The customer sequence is particularly hard to ignore. Eli Lilly announced a collaboration with Chai early this year. Pfizer followed with a license covering Chai-3 and a custom model. Novartis expanded more than a year of technical work into antibody discovery across several programs. argenx then adopted the platform for de novo antibody work.
More recently, Bristol Myers Squibb became the fifth major biopharma company publicly tied to Chai's platform. BMS said it would use Chai's folding and design models across antibody discovery efforts and as part of a continuously learning discovery system.
That latest agreement is useful because it arrived after Chai's huge Series C. The customer activity did not simply peak before investors marked the company up.
Several competitors beat Chai on individual metrics. None of the independent startups we reviewed is currently accelerating across this many dimensions at the same time.
If you want more recent data on this point, please see our latest AI in drug discovery market report.
How fast has Chai Discovery’s valuation grown?
Chai Discovery’s valuation has risen extraordinarily fast, with the company moving from roughly $550 million around its Series A to $3.8 billion in less than a year.
The cleanest comparison starts with Chai's Series B. The company raised $130 million at a $1.3 billion valuation in late 2025. About seven months later, its $400 million Series C valued Chai at $3.8 billion.
That is a 2.9-fold valuation increase in roughly seven months.
Going back one financing further makes the curve steeper. Chai's $70 million Series A was reported at about $550 million. From there to $3.8 billion represents an increase of roughly 6.9 times.
Even the frequency of the rounds is unusual. Chai closed three substantial venture rounds in less than a year, with the $400 million Series C alone accounting for close to two-thirds of all disclosed capital the company has raised.
Private-market valuation is partly a bet on what Chai may become. We have no evidence that its revenue grew sevenfold alongside the valuation. But the repricing happened while new pharmaceutical customers were arriving and its models were moving from evaluation into actual discovery workflows, which gives the increase more substance than a funding story on its own.
| Chai financing | Amount raised | Valuation |
|---|---|---|
| Series A | $70M | ~$550M |
| Series B | $130M | $1.3B |
| Series C | $400M | $3.8B |
This chart, featured in our AI in drug discovery market deck, shows annual venture capital investment in AI drug discovery startups
Are Chai Discovery’s pharma deals real adoption or mostly AI publicity?
Chai Discovery’s pharma deals increasingly look like real adoption, because several customers are putting Chai models inside existing discovery workflows rather than running vague exploratory pilots.
Lilly provides the clearest early example. Before announcing its collaboration, Lilly had already evaluated molecules generated by Chai. It then agreed to deploy Chai's platform across multiple biologics targets and have Chai build a model trained on large amounts of proprietary Lilly data.
Pfizer went further than simple software access as well. Its scientists received Chai-3, custom software and a model using Pfizer's own data that was tailored to Pfizer workflows.
Novartis is useful for a different reason. Its public agreement came after more than a year of technical engagement with Chai, including early access to a next-generation folding model. The companies then expanded the work into antibody discovery across multiple therapeutic programs.
argenx brings credibility from a company built around antibody discovery, while the more recent Bristol Myers Squibb agreement broadens Chai's reach again.
We should still be careful with the word "customer." Financial terms have generally remained private, so we cannot compare the value of these contracts cleanly. What we can see is unusually fast movement from model evaluation to proprietary-data integration and wider deployment.
| Pharma company | How Chai is being used | What we learn from it |
|---|---|---|
| Eli Lilly | Multiple biologics targets and Lilly-specific model | Chai moved beyond external model access |
| Pfizer | Chai-3 plus custom model using Pfizer data | Software is entering Pfizer's discovery workflow |
| Novartis | Antibody programs across multiple targets | Relationship expanded after >1 year of technical work |
| argenx | De novo antibody discovery | Specialist antibody developer is testing Chai directly |
| Bristol Myers Squibb | Folding and design models for antibody discovery | Chai kept adding pharma adoption after its Series C |
Did Chai-2 actually change Chai Discovery’s trajectory?
Yes. Chai-2 appears to be the technical breakthrough that turned Chai Discovery from a promising research company into something big pharma wanted to test quickly.
In the Chai-2 bioRxiv paper, the company tested its model against 52 targets that had no existing antibody or nanobody binder in the Protein Data Bank. Researchers generated no more than 20 designs per target.
Chai reported a 16% average antibody hit rate and found at least one successful binder for half of the 52 targets. The whole design-to-wet-lab-validation cycle took under two weeks.
The scale of the experimental screen is the interesting part. Drug discovery teams have traditionally screened very large libraries to find promising binders. Chai was claiming useful hit rates from tiny batches generated directly from the molecular target.
Forbes later reported that close to 20 pharmaceutical companies contacted Chai after the Chai-2 work came out. The burst of pharma agreements that followed makes that reaction more than an anecdote.
Chai subsequently tested full-length monoclonal antibodies and reported that more than 86% of those designs showed strong developability characteristics comparable with therapeutic antibodies. Experimentally determined structures of selected designs also closely matched the structures predicted by the model.
We still have to treat those results correctly. Both papers came from Chai researchers, and the authors disclosed that they may own Chai shares. Chai-2 has produced impressive experimental evidence, but independent validation remains thinner than the commercial enthusiasm surrounding the company.
This chart, featured in our AI in drug discovery market deck, shows how Shrödinger is positioned in AI drug discovery
Is Chai-3 actually good enough to explain why pharma companies are adopting Chai?
Chai-3 probably explains part of Chai Discovery’s recent demand, although the public evidence around the model is still less mature than the customer adoption.
According to Chai, Chai-3 roughly doubles the success rate of Chai-2 and can generate antibodies that bind far more tightly to their intended targets. Forbes reported that the model produces therapeutic-level binding affinities in roughly half of cases, based on company data.
Those are large improvements if they hold up.
Pfizer's decision gives the claims more weight. The drugmaker signed for access to Chai-3 and a customized system built around Pfizer's proprietary data. Novartis also included Chai-3 in the wider deployment announced after its long technical engagement with Chai.
Still, we can be firmer about Chai-2 than Chai-3 today. Chai-2 has detailed public experimental papers that we can inspect. Chai-3's most impressive numbers currently come mainly from company statements and reporting around commercial agreements.
So the current adoption tells us sophisticated pharma teams see enough value to use Chai-3. We do not yet have enough independent public evidence to declare it the best molecular design model in the market.
Can Chai Discovery really lead AI drug discovery without a drug in clinical trials?
Yes, Chai Discovery can be the fastest-growing AI drug discovery startup today even though it has not publicly taken a Chai-designed medicine into human trials.
Chai is scaling more like a technology platform than a traditional biotech pipeline.
A biotech developing its own drug has to move sequentially through discovery, preclinical work, regulatory filings and clinical trials. Chai can work on different targets at Lilly, Pfizer, Novartis and other companies at the same time.
That parallel model helps explain why commercial growth can happen so quickly. One improvement to the underlying model can theoretically be used across many discovery programs without Chai having to finance every resulting medicine itself.
But there is a clear ceiling to what this proves.
Designing better molecules early in discovery would be valuable even if Chai never becomes a drug company. It still leaves the industry's biggest question unanswered: do medicines designed with these models survive clinical development more often?
For that question, Iambic is already further ahead.
This chart, featured in our AI in drug discovery market deck, shows annual funding in AI drug discovery startups
Is Iambic Therapeutics ahead of Chai Discovery where it matters most?
Iambic Therapeutics is currently ahead of Chai Discovery on clinical proof, because Iambic has already put an AI-designed drug into patients and reported early evidence that the molecule is active.
IAM1363, Iambic's HER2 inhibitor, went from program start to clinical testing in about two years. Iambic says that compares with roughly six years for the industry average.
The Phase 1/1b trial has since expanded internationally. More importantly, Iambic presented actual patient data at the 2025 ESMO Congress.
Among 18 evaluable patients with measurable systemic disease treated at the specified higher doses, 28% had partial responses. The dataset was small and early, so nobody should read that as proof the drug will ultimately work. It is still a much harder test than designing molecules in a lab.
Iambic has also been adding outside validation. Takeda signed a multi-year deal spanning small-molecule programs in oncology and gastrointestinal and inflammatory diseases. The agreement includes access to Iambic's NeuralPLexer model and potential success-based payments above $1.7 billion.
This makes Iambic the strongest challenge to our Chai conclusion.
If someone asks, "Which AI drug discovery startup is proving fastest that AI can make actual medicines?" we would choose Iambic over Chai today.
The title asks which startup is growing fastest overall, and Chai's company-wide acceleration is currently stronger.
If you want more recent data on this point, please see our latest AI in drug discovery market report.
Is Genesis Molecular AI turning AI drug discovery into pharma cash faster than Chai?
Genesis Molecular AI has stronger disclosed evidence of immediate pharma economics than Chai Discovery, especially after Incyte dramatically expanded their collaboration.
The numbers are unusually concrete.
Incyte agreed to give Genesis $120 million when the collaboration expanded: an $80 million non-refundable upfront payment plus a $40 million equity investment.
Incyte's SEC filing also gives us something most AI drug discovery announcements hide. For the initial new target programs, Genesis can receive up to $135 million of development milestones, $475 million of regulatory milestones and $550 million of sales milestones.
The expansion followed an earlier two-target relationship. It added at least five new targets and allows Incyte's proprietary experimental data to train the next generation of Genesis's GEMS platform.
Genesis also has a Gilead collaboration that began with a $35 million upfront payment.
That makes Genesis particularly interesting. We are seeing a pharmaceutical company come back after an initial collaboration, commit substantially more money, widen the number of programs and feed proprietary data into the startup's AI system.
Chai has added more headline customers lately. Genesis currently gives us much clearer evidence that at least one pharma relationship is turning into substantial near-term cash.
This chart, featured in our AI in drug discovery market deck, compares the main business model options for AI drug discovery biotech companies
Is Isomorphic Labs actually growing faster than Chai Discovery?
Isomorphic Labs is growing faster than Chai Discovery in absolute capital and scale, but calling Isomorphic the fastest-growing startup would stretch the word "startup" too far.
Isomorphic raised $600 million externally in 2025 and followed it with a $2.1 billion Series B this year. That gives the company $2.7 billion across those two financings alone.
No independent company in this comparison comes close.
The pharmaceutical side is substantial as well. Isomorphic has major relationships with Novartis and Eli Lilly, and Novartis expanded its original collaboration to additional programs. Johnson & Johnson has also signed a multi-target research collaboration spanning several molecular types.
Isomorphic says the new capital will help move its own therapeutic programs toward the clinic while expanding its AI drug design engine globally.
The catch is obvious once we look at where the company came from. Isomorphic was created from DeepMind, remains owned by Alphabet and has direct links to one of the strongest AI research organizations in the world. Alphabet and GV also participated in the latest financing.
So Isomorphic is currently the bigger AI drug design operation by capital. Chai has climbed much faster from an independent startup base.
For the specific title of this article, we give Chai the edge.
If you want more recent data on this point, please see our latest AI in drug discovery market report.
Could Profluent or Xaira Therapeutics overtake Chai Discovery?
Profluent looks like the more immediate challenger to Chai Discovery on commercial acceleration, while Xaira Therapeutics remains the bigger long-term wildcard.
Profluent's strongest move came through Eli Lilly. The companies signed an agreement to develop AI-designed recombinases for genetic medicines, with potential payments reaching $2.25 billion if the programs achieve their milestones.
The startup has also pushed beyond ordinary protein prediction. Its models are designed to generate new proteins, including gene-editing systems, and its OpenCRISPR work demonstrated an AI-designed gene editor working in human cells.
More recently, Profluent's technology became part of programs backed by ARPA-H, adding another route toward medical applications. It still lacks the same breadth of named pharmaceutical deployment that Chai has built.
Xaira is almost the opposite case.
Xaira launched with close to $1 billion in committed capital and is trying to build a full AI-native biotechnology company rather than a narrow software platform. Its X-Cell work focuses on predicting how cells respond to interventions, while its broader organization combines AI, biological data generation, discovery and eventual clinical development.
The company has continued hiring senior discovery, translational and clinical-development leaders lately, which shows it is building toward a drug pipeline rather than staying mainly a research organization.
But Xaira was born enormous. Its initial financing tells us more about its starting resources than its current growth rate.
Neither company currently knocks Chai out of first place, although Profluent could change the commercial ranking quickly with another major pharma adoption and Xaira could change the scientific ranking once its internal programs become visible.
This chart, featured in our AI in drug discovery market deck, shows revenue breakdown by customer segment in the AI in drug discovery market
Are billion-dollar AI drug discovery deals making startups look bigger than they really are?
Yes, billion-dollar AI drug discovery deals routinely exaggerate how much money startups are actually receiving today.
The biggest number in a biotech collaboration usually includes years of possible development, regulatory and sales milestones.
Profluent's Lilly agreement can reach $2.25 billion. Iambic can receive more than $1.7 billion in success-based payments from Takeda. Genesis's Incyte relationship contains more than $1 billion of possible milestones for the first set of additional programs.
Those are meaningful contracts, but they cannot be treated like current revenue.
Genesis gives us the best view behind the headline. Incyte disclosed $80 million of non-refundable upfront cash and a separate $40 million equity investment. The rest depends on future work and successful drug development.
That is why we rank repeat adoption, upfront payments and wider deployment more heavily than maximum theoretical deal value.
A $2 billion agreement can be strategically important while producing a much smaller amount of revenue in its first year.
| Company | Headline collaboration value | What is clearly near-term |
|---|---|---|
| Profluent / Lilly | Up to $2.25B | Full upfront economics undisclosed |
| Iambic / Takeda | >$1.7B potential success payments | Upfront, research and access payments undisclosed |
| Genesis / Incyte | >$1B potential milestones on initial new programs | $80M upfront cash + $40M equity investment |
Is Chai Discovery’s $3.8 billion valuation getting ahead of the actual business?
Probably. Chai Discovery’s valuation is currently growing faster than anything we can directly measure in its revenue, so investors are clearly pricing in a lot of future success.
Chai does not disclose enough financial information for us to show that revenue rose at anything close to the pace of its private valuation.
Its pharma agreements are generally private. We do not know what Pfizer, Novartis, argenx or Bristol Myers Squibb are paying. Chai also has no publicly disclosed clinical-stage medicine that would let us value a proprietary drug pipeline.
So $3.8 billion requires a big assumption: that molecular design models will become an important layer inside pharmaceutical R&D and that Chai will remain one of the companies supplying that layer.
There is at least more behind the valuation than there was several months ago. Chai has gone from an initial Lilly relationship to multiple large pharmaceutical deployments, while the newest agreement arrived after the financing rather than before it.
The risk is that early discovery software may prove easier to adopt than to monetize at enormous scale. Another risk is technical competition. DeepMind and Isomorphic remain formidable; other protein-design models are improving quickly; large pharmaceutical companies are also building their own AI capabilities.
We therefore think Chai is growing fastest without concluding that its latest valuation is cheap or obviously justified. Those are two different claims, and the second one requires financial evidence Chai has not disclosed.
If you want more recent data on this point, please see our latest AI in drug discovery market report.
This chart, featured in our AI in drug discovery market deck, shows how AI drug discovery platform technology has evolved over time
Would Insilico Medicine win if we ignored the word “startup”?
Yes. If we include public companies, Insilico Medicine currently has a stronger combination of measurable financial growth and clinical progress than any private company in this comparison.
Insilico's latest interim results are one of the freshest hard data points in the entire AI drug discovery sector.
The company reported $106.3 million of revenue for the first half of 2026, up 287.2% from the same period a year earlier. Gross margin reached 90.3%, net profit was $35.5 million and adjusted net profit exceeded $51 million.
Drug discovery and pipeline-development activities generated about $103.1 million, more than triple the prior-year figure. Insilico said large upfront payments from business-development deals and continuing milestone payments drove much of the increase.
The clinical side is even harder for younger startups to match.
Rentosertib, Insilico's treatment for idiopathic pulmonary fibrosis, has entered Phase 3 after positive Phase 2a work. The program originated from an AI-identified biological target and an AI-assisted molecule discovery process.
Insilico therefore gives us something Chai cannot yet show: rapidly growing reported revenue, positive profit and a late-stage drug program inside the same AI-driven company.
Its recent results also help calibrate the rest of the sector. Private startups can talk about multibillion-dollar partnerships and valuations, but Insilico now gives us an example of what actual commercialization looks like once those deals begin producing reported financial results.
Insilico does not win our startup ranking because it is already a public company. If the title simply asked which AI drug discovery company is growing fastest, the answer would be much less comfortable for Chai.
So which AI drug discovery startup is growing the fastest today?
Chai Discovery is currently the fastest-growing independent AI drug discovery startup we found, but Iambic, Genesis and Isomorphic each expose an important limit to that answer.
Chai wins because several things accelerated together.
Its model performance improved enough to trigger intense pharma interest. Pharmaceutical relationships quickly moved into real workflows, including proprietary-data models and multi-program deployments. Its funding accelerated at the same time, culminating in a $400 million Series C.
And the momentum has continued rather than fading after the fundraise.
Chai's valuation reached $3.8 billion after sitting at $1.3 billion roughly seven months earlier. That repricing would look much shakier without the customer activity around it. Together, the two curves make Chai the clearest current answer.
But the runner-up depends on what we care about.
Iambic has gone further in proving that an AI-designed molecule can survive the trip into human testing and show early activity. Genesis has better disclosed evidence of large upfront pharmaceutical payments. Isomorphic has far more capital and may ultimately build the largest AI drug design organization of the group, although its Alphabet ownership makes the startup comparison messy.
Profluent is moving quickly enough to watch closely, particularly after its Lilly deal. Xaira has enormous resources but still needs to show what those resources are producing.
And Insilico has already moved beyond the startup argument entirely: its recent revenue growth, profitability and Phase 3 progress make it the strongest operating benchmark in the sector.
So the answer is sharper than before. Chai Discovery is growing fastest as an independent AI drug discovery startup today. Iambic is ahead in clinical translation. Genesis is currently stronger on disclosed near-term pharma economics. Isomorphic leads in capital scale. Insilico shows what the next stage of the business could look like if the AI drug discovery model really works.
| What are we measuring? | Current leader | Why |
|---|---|---|
| Overall independent startup growth | Chai Discovery | Fastest combined customer, valuation and deployment expansion |
| Clinical progress among private startups | Iambic Therapeutics | AI-designed medicine already producing early human data |
| Disclosed pharma cash economics | Genesis Molecular AI | Large upfront payment plus expanded repeat collaboration |
| Capital scale | Isomorphic Labs | By far the largest recent private financing |
| Emerging challenger | Profluent | Large Lilly deal plus AI-designed gene-editing technology |
| Broader company growth | Insilico Medicine | Triple-digit reported revenue growth, profit and Phase 3 progress |
If you want more recent data on this point, please see our latest AI in drug discovery market report.
In our AI in drug discovery market deck, we identify pain points entrepreneurs should prioritize
OUR METHODOLOGY
We approached “Which AI drug discovery startup is growing the fastest?” as a comparison problem rather than a search for one convenient growth metric. The companies in this market can expand through pharma adoption, deeper deployment, financing, technical progress, clinical translation or partnership economics, and no single measure captures all of that well.
We therefore broke the question into those dimensions and looked for the freshest evidence in each one. We gave more weight to changes in the underlying business: repeat collaborations, additional programs, proprietary-data integration, movement into real pharma workflows, experimental results, human clinical data and clearly disclosed upfront economics.
For biotech partnerships, we separated money that is clearly near-term from milestone packages that may take years to earn. A multibillion-dollar maximum deal value can be strategically important, but it is not the same thing as current revenue or cash received.
Scientific evidence was treated separately from commercial adoption. We prioritized experimental and clinical results over model claims alone, and we distinguished detailed public studies from results available mainly through company statements. Pharma adoption is useful evidence that sophisticated teams see value in the technology, but it is not independent scientific validation by itself.
Company structure also matters. The core ranking focuses on independent private startups. Isomorphic Labs remains an important benchmark because of its scale, but its Alphabet ownership and DeepMind origins change how we interpret its financing. Insilico Medicine is used as an operating benchmark rather than ranked as a startup because it is now publicly listed.
Private-market valuation was used as supporting evidence, not as a substitute for operating growth. Chai’s repricing matters more because it happened alongside expanding pharma relationships and deployment; we do not assume its revenue grew at anything close to the same rate.
Key sources include Chai Discovery on its Series C, Chai on its Series B, Chai on its Series A, Chai on Bristol Myers Squibb, Chai on argenx, Chai’s Chai-2 results, the original Chai-2 bioRxiv paper, and the follow-up monoclonal-antibody paper.
For the comparison set, we also used Iambic’s IAM1363 clinical update, Iambic’s Takeda collaboration, Incyte’s disclosure on the expanded Genesis collaboration, Incyte’s SEC filing, Isomorphic Labs on its latest financing, Profluent’s primary media disclosures, Xaira Therapeutics on its launch and company model, and Insilico Medicine’s first-half 2026 results.
The final ranking comes from the aggregation of those recent observations rather than one standout number. That is why Chai leads overall while Iambic leads on clinical translation, Genesis on disclosed near-term pharma economics, Isomorphic on capital scale and Insilico on broader operating proof.
This chart, featured in our AI in drug discovery market deck, shows revenue breakdown by region across Europe, Asia, North America, Africa, and South America in the AI in drug discovery market
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