Artivila Biopharma Joins the AI Drug Discovery IPO Queue with A-round Funding and Pipeline Progress

Jul 03, 2026 16:51 CST Updated 16:51

Artivila

Innovative Drug Developer

Recently, AI drug discovery company Artivila（Artivila Biopharma）Announced the completion of a Series A financing round of nearly RMB 100 million, led by Sinovest Capital, with participation from Junyi Investment and others. Kaicheng Capital served as the exclusive financial advisor for subsequent financing rounds. To date, Artivila’s cumulative fundraising has reached nearly RMB 400 million.

Notably, the financing announcement specifically mentioned laying the groundwork for a future IPO.

A company founded in 2018 and currently at Series A has already included an IPO in its financing press release. This is not an isolated case. Currently, the AI-driven drug discovery sector is witnessing a concentrated capital narrative of companies queuing for IPOs: some have already successfully rung the opening bell, others are sprinting toward listing, and still others are laying the groundwork for an IPO in advance.

This reflects that the AI drug discovery sector is entering a new stage of development.

Pivotal bioVenture China(Biwo Investment Capital)Dr. Liu Dan, Managing Partner, told Xieyi Jun that the team has long focused on the AI-driven drug discovery sector and remains bullish on its development.

“At present, the AI drug discovery IPO landscape has taken on a clearly tiered structure: the top three companies are already listed, two or three firms in the second tier are in the pipeline, and further down are a batch of enterprises with notable technological and business-model strengths but still requiring time to mature. 2026 is a critical window for AI drug discovery companies to list on the Hong Kong stock market, but it also represents a watershed moment—the industry is shifting from ‘storytelling’ to ‘delivering results,’” Dr. Liu Dan further analyzed.

So, at what stage has this sector currently arrived?

TONACEA

The "Three Little Dragons" That Have Already Succeeded

Three AI-driven drug discovery companies have emerged as listed entities in the Hong Kong stock market, collectively referred to within the industry as the “Three Little Dragons.”

The first company is XtalPi. Founded in 2015, its three co-founders are all graduates of the Massachusetts Institute of Technology (MIT). In June 2024, XtalPi listed on the Hong Kong Stock Exchange under Chapter 18C, becoming the first company to complete an initial public offering (IPO) on the exchange in accordance with the Chapter 18C rules.

XtalPi follows the “AI + CRO” model, not selling drugs directly but providing R&D services to pharmaceutical companies, such as AI-driven drug discovery platforms and intelligent robotic laboratories.

In 2025, the company achieved revenue of RMB 803 million, a year-on-year increase of 201.2%, and recorded its first full-year profit, becoming the first Hong Kong-listed company in the AI for Science sector to achieve profitability.

The second company is Insilico Medicine. In December 2025, the company successfully listed on the Hong Kong Stock Exchange, becoming another publicly traded enterprise in the AI-driven drug discovery sector.

Unlike XtalPi, Insilico Medicine follows an “AI+Biotech” strategy, leveraging AI for new drug development and generating revenue through pipeline licensing.（BD）Achieve commercialization. At the time of its listing, the company introduced 15 cornerstone investors, including Eli Lilly, Tencent, Temasek, Schroders, and UBS, marking the first time that both Eli Lilly and Tencent served as cornerstone investors in a biopharmaceutical company.

The third is Jitai Technology, the most recently listed company. In May this year, the company officially debuted on the Hong Kong Stock Exchange. On its first day of trading, its share price surged more than 180% above the offering price, pushing its market capitalization beyond HK$34 billion. During the subscription period, it received 6,900 times oversubscription, making it one of the most heavily oversubscribed IPOs on the Hong Kong Stock Exchange since 2026.

Unlike most AI-driven pharmaceutical companies that focus on drug discovery, MetaDrug has chosen a more specialized niche in “AI + Nano Drug Delivery,” aiming to address the critical challenge of “how to precisely deliver drugs to their targets.”

At this point, XtalPi represents AI-driven drug discovery, Insilico Medicine represents AI-powered in-house R&D pipelines, and Jitai Technology represents AI-enabled drug delivery—these three companies’ differentiated strategic layouts essentially cover the key segments of the AI pharmaceutical industry chain.

Discussing the development of the AI-driven drug discovery industry, Dr. Liu Dan told Xieyi Jun, “We have observed that AI-driven drug discovery has indeed achieved cost reduction and efficiency improvements in certain stages. We have also seen the emergence of new application scenarios, such as the ability of AI to design protein molecules de novo, and the integration of AI with automated dry and wet lab experiments, which has significantly enhanced the efficiency of early-stage drug discovery.”

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Second Tier, Begin Sprint

Beyond the “Three Little Dragons,” multiple companies in China’s AI drug discovery sector have reported substantive progress toward initial public offerings (IPOs).

Among them, Huashen AI Pharma is at the forefront.

In March this year, market sources reported that this AI-driven large-molecule drug R&D company was preparing for an IPO in Hong Kong with CICC and Morgan Stanley, aiming to raise up to $500 million, with the overseas affiliate Earendil Labs as the entity submitting the application.

Underpinning this progress are the company’s realized commercial achievements: Since 2025, it has entered into two licensing collaborations with Sanofi, with a total value exceeding $4.4 billion; in March of this year, it completed a $787 million financing round, setting a new global record for biotechnology company fundraising in 2026.

Speaking of Huashen Zhiyao, Dr. Liu Dan expressed to Xieyi JunShow，“Huashen Zhiyao may list in Hong Kong through Earendil Labs, registered in Delaware, USA. This is a proactive compliance structure design to avoid geopolitical risks amid the US-China decoupling under the Biosecurity Act; its fundamentals are also strong, backed by long-term collaboration with Sanofi.”

News from BioMap followed closely.

Also in March this year, market sources reported that the company co-founded by Robin Li had secretly submitted a listing application to the Hong Kong Stock Exchange, with expectations of raising hundreds of millions of U.S. dollars.

In fact, as early as June 2025,BioMapCEO Liu Wei publicly stated that the company aims to pursue a listing in Hong Kong within approximately 18 months. However, as of now, Baitu has not responded to rumors regarding the submission of its listing application, so the relevant information remains to be further confirmed.

DeepPotential Technology has also signaled its intention to launch an IPO.

In the first half of this year, market reports indicated that the company had completed its joint-stock reform, changed its name to “Beijing DeepModel Technology Co., Ltd.,” and converted its corporate structure to a “joint-stock limited company,” which was regarded as a significant signal of the initiation of an initial public offering (IPO).

However, there is currently no official information confirming its specific market launch progress.

Dr. Liu Dan pointed out that DeepModeling completed its Series C financing round at the end of last year and is actively exploring an IPO path, although there has been no public reporting yet. Strictly speaking, it is a platform-based company spanning pharmaceuticals, materials, and energy, rather than a pure AI-driven drug discovery company.

Beyond the already-listed “Three Little Dragons,” companies such as Huashen Zhiyao, BioMap, and DP Technology have successively signaled their push for Hong Kong Stock Exchange listings, swelling the ranks of AI-driven drug discovery firms pursuing IPOs.

In contrast, Artivila represents an earlier cohort of companies. Although some of its pipeline assets have made progress, it is currently in the Series A financing stage and still has a considerable way to go before an IPO.

When discussing the next tier of candidates, Dr. Liu Dan told Xieyi Jun, “In addition to these companies, we have observed that financing in the AI-driven drug discovery sector has indeed been quite active recently. We see some standout enterprises across different model capabilities, business models, and focused disease areas or modalities. But frankly speaking, they still need more accumulation before they are ready for an IPO.”

As Dr. Liu Dan stated, the AI drug discovery sector continues to gain momentum, with a wave of new companies emerging across areas ranging from foundational models and protein design to various disease indications and technological approaches. For instance, the recent announcement by Harbour BioMed and Bioto Genesis to jointly establish MegaStream, an AI-driven large-molecule drug development company, further underscores their commitment to AI-enabled innovative drug R&D, reflecting the ongoing concentration of industry resources in this field.

TONACEA

Why are they all Hong Kong-listed stocks? Who will ultimately emerge as the winner?

These AI-driven drug discovery companies in the queue are highly likely to converge on the same destination in the future—the Hong Kong Stock Exchange.

The reason is not complicated.

AI-driven pharmaceutical companies typically face long R&D cycles, high capital requirements, and extended timelines to profitability. In contrast, Chapter 18C of the Hong Kong Stock Exchange Listing Rules is specifically designed for “Specialist Technology Companies,” allowing pre-revenue hard-tech enterprises to list, thereby providing AI drug discovery firms with a more viable path to capital markets. Both XtalPi and DTI have listed on the Hong Kong Stock Exchange under this framework, emerging as key beneficiaries of the Chapter 18C regime.

Meanwhile, Hong Kong stocks have become more attractive to international capital.

At its IPO, Insilico Medicine introduced 15 cornerstone investors, including Eli Lilly, Tencent, and Temasek; DrugAI secured support from institutions such as BlackRock and UBS; meanwhile, rumors suggest that Huashen Zhiyao is preparing for an IPO with CICC and Morgan Stanley serving as joint sponsors, continuing its strong international character.

In contrast, there have been no listings of pure-play AI drug discovery companies on the STAR Market and ChiNext Board of China’s A-share market. Influenced by multiple factors including profitability requirements, valuation frameworks, and investor composition, the Hong Kong Stock Exchange has become the preferred venue for IPOs by AI-driven pharmaceutical companies at this stage.

However, compared to the IPO itself, investors are now more concerned with whether companies can deliver on their technological value.

Regarding the current investment logic for AI-driven pharmaceutical companies, Dr. Liu Dan told Xieyi Jun that different investors, due to varying fund characteristics and risk appetites, adopt markedly different approaches to evaluating AI drug discovery. Technology-focused funds tend to prioritize the disruptiveness of platforms and algorithms; healthcare-focused funds place greater emphasis on clinical data and pipeline value; long-term industrial capital is more concerned with business development (BD) synergies and strategic positioning; while state-owned funds focus on national strategic layouts and the long-term developmental trends of innovative paradigms. Nevertheless, a common trend is emerging: investment logic is shifting from an early-stage focus on “technology platforms” toward an emphasis on “value realization.”

“Initially, investments were driven more by optimism about AI technology itself; now, we place greater emphasis on clinical progress and commercial implementation,” stated Dr. Liu Dan candidly.

In concrete assessments, commercial viability remains the most realistic hurdle. Dr. Liu Dan stated, “We first ask a very fundamental question: How do you sustain your business? This is now the primary dividing line that distinguishes leaders from followers.”

In his view, different business models correspond to different development paths:Either the “AI + CRO + incubation” model exemplified by XtalPi, which boasts strong cash flow, low risk, and current profitability; or the in-house pipeline plus business development (BD) partnership model adopted by Insilico Medicine, which carries higher risk but offers greater upside potential.

Dr. Liu Dan further pointed out, “Of course, there is also divergence here—some investors take the opposite view, believing that placing too much emphasis on profitability at this stage could stifle companies with genuine disruptive potential that are still burning cash. Therefore, they are willing to pay a premium for long-term growth prospects. This is a reasonable divergence.”

Beyond the business model, Dr. Liu Dan believes that clinical data, business development (BD) capabilities, and data moats are also key indicators for evaluating the value of AI-driven drug discovery companies. Additionally, factors such as platform differentiation, team competency, cash runway, and geopolitical considerations are all focal points in investors’ evaluation frameworks.

He pointed out that AI has already demonstrated efficiency advantages in the drug discovery phase, but the industry ultimately still needs to withstand the test of clinical data. “To date, no new drug truly discovered and designed under AI leadership has successfully been marketed globally, and the next few years will mark the first window of time for this batch of AI-designed molecules to undergo large-scale clinical testing.”

Meanwhile, the willingness of multinational pharmaceutical companies to pay upfront and milestone payments for business development (BD) collaborations serves as the most compelling third-party validation. However, Dr. Liu Dan stated bluntly, “But we focus on locked-in cash and tangible products, not the potential total value.”

In the long run, what truly determines competitiveness is data barriers. Algorithms are merely the ticket to entry, while the data flywheel constitutes the real moat.

According to Dr. Liu Dan, while AI algorithms themselves undergo continuous iteration, the true moat lies in the ability to establish a self-reinforcing loop: AI-driven design, automated laboratory testing, and real-time feedback of data back into model iteration. In China, however, there are still certain barriers to accessing medical data; whether these data silos can be gradually broken down will become the most significant variable affecting this competitive advantage.

— Xieyi Summary—

From the IPO frenzy to multi-billion-dollar BD deals, and with financing activity remaining robust, AI-driven drug discovery is entering a new phase characterized by the resonance of capital and industry.

Meanwhile, the industry is entering a new phase of value validation. Dr. Liu Dan believes that the current valuations across the AI drug discovery sector implicitly include a “faith premium,” and whether this premium can be realized will ultimately depend on clinical data. However, he also emphasizes that the value of AI in pharmaceuticals does not necessarily need to wait until the first AI-discovered drug reaches the market to be proven; “the value of AI can already be realized through improved efficiency in early-stage discovery.” In his view, these two perspectives are not contradictory—the difference lies in whether investors choose near-term certainty or long-term potential.

In the future, as more clinical data becomes available and commercialization continues to advance, the AI-driven drug discovery sector will enter a more mature phase of development.

Highlights:

Review of Pharmaceutical Policies in the First Half of 2026: Incentivizing Genuine Innovation, Expanding Multi-Payer Systems, and Tightening Compliance Across the Entire Value Chain
Eight Blockbuster New Drugs Race for Launch in H2 2026, Reshaping Multiple Therapeutic Areas
From Follower to Leader: China’s Mid-2026 Report Card on Innovative Drugs (With List of Innovative Drugs)
Securing Two “World’s First Approvals” in a Single Day, Chinese Innovative Drugs Break into the Global “No-Man’s Land”

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