Guojin Securities released a research report stating that Insilico (03696) was listed on the Hong Kong Stock Exchange on December 30, 2025, with an IPO price of HKD 24.05, raising a total of approximately HKD 2.35 billion. About 48%, 15%, 12%, and 20% of the proceeds will be allocated to advancing core pipeline clinical trials, the Pharma.AI platform, automated laboratories, and early-stage research and other pipeline development, respectively. The company is projected to achieve revenues of USD 161 million, USD 224 million, and USD 301 million in 2026, 2027, and 2028, representing year-on-year growth of +186%, +39%, and +34%, respectively. Net profit attributable to shareholders is forecasted at -USD 3 million, USD 27 million, and USD 30 million for the same periods, with a year-on-year increase of 12.14% in 2028. Corresponding EPS is estimated at -USD 0.01, USD 0.05, and USD 0.05. The firm initiates coverage with a "Buy" rating. Guojin Securities' key views are as follows:
Validation: The globally leading Pharma.AI platform has generated 30 PCCs (preclinical compounds), with the first-in-class TNIK inhibitor nearing Phase 3 trials. ① The AI drug discovery industry is entering a clinical validation phase, where success metrics are shifting from model performance to tangible results. ② The company leads in both pipeline diversity and clinical progress. Its average development time per PCC is 12-18 months, significantly shorter than the traditional average of 4.5 years. ③ Rentosertib, the world's first clinical drug targeting the novel TNIK target developed via AI, has released Phase 2 clinical results. It is a potential blockbuster drug that may reverse idiopathic pulmonary fibrosis (IPF). The current annual sales of the existing IPF monotherapy, nintedanib, exceed USD 4 billion.
Barriers: The data-model flywheel effect strengthens over time, creating a winner-takes-all dynamic in this high-profile sector. (1) Competition in AI drug discovery has evolved from model capability to stratification based on closed-loop data capabilities. (2) The company's moat lies in its closed-loop experimental data, where the data-model flywheel drives systematic output. (3) The sustainability of its innovative lead stems from data compounding and path dependency, with the Matthew effect continuously reinforcing its structural advantage. Since 2021, the total contract value of the company's major out-licensing, co-development, and research collaboration agreements has exceeded USD 7.5 billion (the realization timeline remains uncertain).
High Growth with Stability: (1) As a sustainably high-growth company approaching profitability, its platform subscription and business development revenues endogenously support the potential of its self-developed pipeline. (2) The ongoing upgrade of Pharma.AI and its transformation into an agentic drug research operating system enable a leap from digital space to physical execution. (3) The collaboration with MMAI company Gym represents a powerful alliance. The partnership between the company's specialized AI drug discovery models and globally leading foundational large language models solidifies its leadership and the generalization of its technical capabilities.
Risk factors include share lock-up expiration risks; clinical progress falling short of expectations; market volatility due to industry events; heavy reliance on major clients; and commercial uncertainties.