AI Stock Market: The Rise and Challenges of AI Pharmaceuticals in the Competitive Market
AI Stock Market: The emergence of AI pharmaceutical companies follows a familiar storyline seen in the AI domain. Each company presents a grand narrative about the world of AI pharmaceuticals and showcases their technical prowess through publications in prestigious academic conferences and journals.
The collision between AI and pharmaceuticals has generated immense interest, attracting significant attention and capital investment. AI pharmaceutical companies have become the darlings of the industry, continuously securing financing to support their endeavors.
However, as the market hype grows, so do the challenges these companies face. Regardless of their grand visions, companies ultimately have a fundamental duty to generate profits and provide returns to shareholders. In the secondary market, financial performance is the primary yardstick for success.
Unfortunately, the financial indicators of most AI pharmaceutical companies are not favorable. For instance, Yingsi Intelligence, the first AI pharmaceutical stock on the Hong Kong Stock Exchange, reported a revenue of only $30.14 million in 2022. This falls short of contributing positive cash flow, as the company experienced operating cash outflows of $41.92 million in 2021 and $65.13 million in 2022.
While technology is important, the ultimate goal for AI pharmaceutical companies is to achieve commercial returns. Presently, answering this question proves challenging for most AI pharmaceutical companies, leading to a crisis of faith. In the face of a harsh capital winter, many AI pharmaceutical companies worldwide are struggling to survive. Survival is crucial for these companies to give meaning to their grand narratives. (AI Stock Market)
Limitless Expectations for AI Pharmaceuticals
Every emerging business model arises to address the pain points of existing industries, and AI pharmaceuticals are no exception.
The primary challenge in the development of innovative drugs lies in the anti-Moore’s law phenomenon: as low-hanging fruit is gradually harvested, the development of new drugs becomes increasingly difficult. With an expanding array of treatment options and mounting regulatory requirements, the research and development (R&D) cycle lengthens, resulting in greater difficulties and costs.
AI pharmaceuticals emerged to overcome these challenges. In simple terms, AI pharmaceuticals employ big data and other capabilities to streamline the drug development process and reduce costs.
For instance, Yingsi Intelligence stated in its prospectus that, under normal circumstances, it takes 4.5 years for a drug to enter clinical trials. However, by utilizing their Pharma.AI research and development platform, this timeline can potentially be shortened to just 12 months.
Presently, AI pharmaceutical companies cover almost all aspects of new drug R&D worldwide. Wherever there are challenges in drug development, AI pharmaceuticals are making significant contributions. (AI Stock Market)
However, different companies adopt different business strategies. For example, Exscientia, the leading AI pharmaceutical player in the UK, focuses on target selection and drug design for small molecule drugs. On the other hand, Insilicon Intelligence’s Pharma.AI can be utilized for both small molecule and macromolecular drug research and development.
The captivating narrative surrounding AI pharmaceuticals has made it a global market sensation. In February 2020, Schrodinger went public on the US stock market, experiencing an unprecedented surge in stock price that has since multiplied by almost six times. Subsequently, other companies, such as Relay Therapeutics, also performed exceptionally well after their initial public offerings.
This trend soon spread from the US to China. Domestic AI pharmaceutical companies like Jingtai Technology, Yingsi Intelligence, Bingzhoushi, Baitu Biotech, and Wangshi Zhizhi became highly sought after in the primary market.
As depicted in the chart above, Insilicon Intelligence’s valuation increased from $54.4 million in 2018 to $894 million in 2022, nearly a 20-fold surge. During this period, the company successfully completed seven rounds of financing, turning into a formidable fundraising machine. (AI Stock Market)
This frenzy stems from the unlimited expectations the market holds for AI pharmaceuticals. (AI Stock Market)
AI Stock Market: Challenges in Monetizing Services
After the initial frenzy, companies now face the daunting question of how AI pharmaceuticals can generate revenue.
From a profit model perspective, one of the most popular approaches for AI pharmaceutical companies is to provide AI-related software or solutions to pharmaceutical companies as service providers or platforms.
Selling software or offering services appears to be a promising business model. AI pharmaceutical companies essentially act as Contract Research Organizations (CROs), providing drug discovery services to pharmaceutical companies on-demand, without shouldering the risks associated with innovative drug R&D.
However, the potential ceiling for AI pharmaceutical companies in this model remains uncertain. (AI Stock Market)
This uncertainty stems from the perceived value of AI pharmaceutical services and the willingness of pharmaceutical companies to pay for them.
In terms of service value, while drug discovery is important, it represents a relatively small proportion of the overall drug development cost. According to Schrodinger’s prospectus, the cost of taking a small molecule drug from discovery to clinical approval is around $35 million. Considering the widely recognized R&D cost of $1 billion, drug discovery accounts for a mere 3.5% of the total.
One might argue that 3.5% is still significant considering the vast number of pharmaceutical companies and projects worldwide. However, the willingness of pharmaceutical companies to pay high prices for AI pharmaceutical services remains debatable.
Essentially, AI pharmaceuticals offer a potential increase in the probability of success, but the actual feasibility and effectiveness require extensive validation through follow-up animal experiments and clinical trials. (AI Stock Market)
Consequently, pharmaceutical companies may hesitate to pay premium prices for these services. From a global perspective, Schrodinger currently leads in terms of revenue, reporting $135 million in 2022.
Similarly, Insilicon Intelligence aims to generate revenue by selling services, primarily through licensing the Pharma.AI R&D platform. However, major pharmaceutical companies have shown limited interest in adopting the Pharma.AI platform. In 2022, the revenue from AI software services amounted to only $1.49 million, accounting for less than 5% of the company’s total revenue.
The uncertainties surrounding the revenue ceiling and the willingness to pay create significant challenges for AI pharmaceutical companies operating in what appears to be a highly lucrative market. (AI Stock Market)
Realizing the Pharmaceutical Business
In addition to selling software and services, another profit model for AI pharmaceutical companies is to develop innovative drugs themselves and eventually generate revenue through commercialization or external licensing agreements that include milestone payments.
In this regard, AI pharmaceutical companies are indistinguishable from traditional biotechs. This is currently the core monetization model for Insilicon Intelligence.
The company has already established over 30 pipelines, including TNIK inhibitors, USP1 inhibitors, 3CL main protease inhibitors, QPTCL inhibitors, PHD1/2 inhibitors, intestinal-restricted PHD1/2 inhibitors, TEAD1/2/3/4 inhibitors, ENPP1 inhibitors, KAT6 inhibitors, MAT2A inhibitors, and more. (AI Stock Market)
However, the most promising TNIK inhibitor is still in phase 2, making it unrealistic to discuss commercialization at this stage.
Currently, Insilicon Intelligence’s primary source of income comes from providing external R&D services. They promote AI-based drug research and development for pharmaceutical companies, earning service fees, down payments, and milestone payments.
From this perspective, AI Pharmaceuticals goes beyond the story of AI transforming drug research and development. The core focus lies in the “pharmaceuticals” themselves, rather than merely relying on the “AI” component. (AI Stock Market)
Consequently, the success of AI pharmaceutical companies hinges not only on their AI technologies but also on
a series of subsequent capabilities required in the clinical stages.
As we observe the development of companies like Yingsi Intelligence, the line separating AI pharmaceutical companies from traditional pharmaceutical companies becomes increasingly blurred.
To some extent, the research and development challenges faced by traditional pharmaceutical companies are now shared by AI pharmaceutical companies. In this context, AI pharmaceuticals worldwide are striving for stability. However, armed with AI platforms, they may forge a distinct path forward. (AI Stock Market: The Rise and Challenges of AI Pharmaceuticals in the Competitive Market)