As China's First AI Pharmaceutical Company Goes Public, Investors Are Still Cautious (AI Translation)

【周刊提前读】首家AI药企上市 赛道投资为何仍然谨慎？

The listing of Insilico Medicine, the first Chinese AI pharmaceutical company on the Hong Kong Stock Exchange, has generated significant market interest [para. 1]. On June 13, XtalPi debuted as China’s "first AI pharmaceutical stock" and became the first "Specialized Technology" stock on the HKEX. The IPO price was HK$5.28 per share, raising HK$896 million, with an additional HK$47.31 million raised later in early July, leading to a market valuation of approximately HK$19 billion [para. 2][para. 3].

AI-driven drug discovery, initiated with computer-aided drug design in the 1960s, has recently seen significant advancements due to high-throughput sequencing technology and improved computing power. Globally, over 20 AI-driven pharmaceutical companies are public, categorized broadly into AI+SaaS, AI+CRO, and AI+Biotech, with XtalPi falling under AI+CRO [para. 5]. Post-COVID-19 and Google's release of AlphaFold 2, nearly 100 Chinese AI drug development companies have emerged [para. 6].

XtalPi’s IPO could revitalise investor interest in the pharmaceutical industry, influencing subsequent financing opportunities. Insilico Medicine, another AI+Biotech company, is also preparing for an IPO despite investors' cautious optimism due to the existing capital winter [para. 8]. While AI brings efficiency gains in drug development, a complete disruption seems distant, and no genuine drugs have yet resulted from AI-driven discovery [para. 9][para. 10].

XtalPi's operations focus on providing R&D solutions based on quantum physics, AI, and robotics. The company achieved significant revenue from drug discovery and intelligent automation solutions in 2023, differentiating itself from Insilico Medicine, which focuses on developing therapeutic drug pipelines [para. 11]. XtalPi opted to list under HKEX’s Chapter 18C rules for "Specialized Technology" companies, imposing stricter requirements than those for biotech firms under Chapter 18A [para. 12][para. 13].

XtalPi had completed eight financing rounds, raising $732 million before its IPO in Hong Kong, prompted by the new Chapter 18C rules [para. 14]. Prominent political figures in Hong Kong highlighted the listing's significance, with cornerstone investors including Li Ka-kit and several international investment institutions subscribing to 34.12% of the shares [para. 16]. Investors noticed increased attention to the dual attributes of "medical" and "technology," although XtalPi remains perceived as overvalued based on industry comparisons [para. 17][para. 19].

The listed stock of XtalPi, classified as a Contract Research Organization (CRO), focuses on empowering partners in drug development rather than establishing an independent drug pipeline [para. 20]. The company has made strategic investments in synergistic enterprises, such as Jingtai Medicine and several small molecule drug companies, aligning with its core business strategy of providing innovative R&D platforms [para. 21][para. 22].

Globally, AI advancements in drug discovery continue to progress rapidly. Google's AlphaFold 2 and its subsequent versions have dramatically improved protein structure prediction, fundamental to drug design [para. 28][para. 31]. DeepMind’s AlphaFold and David Baker’s RoseTTAFold represent top technological advancements in the field. Collaborations between AI pharmaceutical firms and major drug companies like Eli Lilly, Novartis, and Pfizer highlight AI’s growing impact [para. 34][para. 36].

Despite the promising potential, investors remain cautious. The investment in AI for pharmaceuticals peaked globally in 2021 and is projected to rebound in 2024 [para. 7]. Investors seek tangible pipeline value in AI-driven drug development, with safety and efficacy data being crucial for valuation, reflecting current market rationality [para. 9][para. 30].

In conclusion, while XtalPi’s IPO may boost investor sentiment, the broader AI pharmaceutical sector faces significant challenges and high expectations to deliver profitable, AI-discovered drugs. Continued technological advances and strategic collaborations with established pharmaceutical firms are key determinants of future success in this burgeoning field [para. 39][para. 40].