Core Viewpoint - AI-driven drug development is transitioning from stage 2.0 to stage 3.0, with significant advancements in target discovery and molecular design through the use of AI algorithms and large datasets [2][15]. Summary by Sections AI in Drug Development - Traditional drug development relies heavily on human knowledge and experience, which has limitations. AI can analyze vast amounts of data to identify novel targets and generate molecules, thus overcoming these limitations [3][5][6]. - The main areas where AI empowers drug development are in discovering new, reliable targets related to diseases and in molecular design, whether for small molecules or antibodies [6][7]. Milestone Projects - A notable project by the company involved developing a compound for idiopathic pulmonary fibrosis (IPF), which took approximately 18 months and cost over $2 million, achieving significant milestones in target discovery and molecular design [9][10]. - The project utilized AI tools to analyze multi-omics data from patients, leading to the identification of a new target, TNIK, and the design of a small molecule to inhibit its activity [10][11]. Current AI Capabilities - Currently, AI can assist in generating results but cannot make decisions. The final decision-making still relies on human scientists [12][14]. - The emergence of large models has improved efficiency in coding and data analysis, but the need for human oversight remains critical [13][14]. Future of AI in Drug Development - The industry is currently at stage 2.0, with the potential to reach stage 3.0 as AI becomes more integrated into the entire drug development process. However, a dedicated AI-driven super-intelligent agent is necessary to advance to stage 4.0 [17][18]. - Data quality and the need for a feedback mechanism from scientists are significant challenges in developing a robust AI drug discovery agent [19]. Competitive Landscape - The future of AI-driven drug development will be dominated by companies that can effectively integrate AI technology into practical applications and find viable commercialization paths [20][22]. - Collaboration between independent AIDD companies and large pharmaceutical firms will be essential, with each playing distinct roles in the drug development ecosystem [22][23].

Core Viewpoint - Insilico Medicine's third attempt to list on the Hong Kong Stock Exchange is crucial not only for its future development but also for the AI pharmaceutical sector to achieve reasonable market valuation and favor [1] Business Model - Insilico Medicine operates in three main segments: drug discovery and pipeline development, software solutions, and other discoveries related to non-pharmaceutical fields [2] - The pipeline drug development includes commercialization of self-developed pipelines post-approval, revenue from licensing candidate drugs, and income from drug discovery collaborations. Currently, the company has no commercialized candidate drugs, with revenue primarily from three licensed candidate drugs [2] - The company utilizes its Pharma.AI platform for drug discovery, charging clients subscription fees for access, with the highest annual subscription fee for hosted software at $200,000 and for local software at $525,000 [2] Financial Performance - Revenue has shown consistent growth from $30.15 million in 2022 to $51.18 million in 2023, and projected to reach $85.83 million in 2024, with a compound annual growth rate (CAGR) of 69% from 2022 to 2024 [4] - Gross margins have improved significantly, rising from 63.4% in 2022 to 90.4% in 2024, with net losses narrowing by nearly 92% over the same period [4][5] - The company reported a net cash outflow from operating activities of $57.4 million in 2024, relying on financing to support R&D [9] Industry Growth - The global AI pharmaceutical market is expected to exceed $5 billion by 2025, with a CAGR of 40%. The market for lung fibrosis drugs is projected to grow at a CAGR of 7.1% from 2023 to 2032 [13] Competitive Landscape - Insilico Medicine differentiates itself through its end-to-end platform, with significant advantages in target discovery, molecular generation, and clinical trials compared to international peers [15] - The company operates under the AI+Biotech model, focusing on self-developed pipelines and covering the entire drug development chain [15] Advantages - The company has a strong technical foundation, with one of its assets in the II clinical phase being the fastest progressing in its field. The Pharma.AI platform significantly reduces drug development timelines [17] - Insilico Medicine has established international collaborations, validating its commercialization capabilities, including exclusive licensing agreements with Exelixis Inc and Stemline Therapeutics Inc [18] R&D Expenditure - R&D expenses have been gradually controlled, with the proportion of R&D costs to revenue decreasing from 259% in 2022 to 107% in 2024 [19] Client and Supplier Concentration - The company has a high concentration of revenue from its top five clients, accounting for 90.6% to 94.4% of total revenue from 2022 to 2024 [22]