Insilico Medicine (03696), a leading AI-driven drug discovery company that went public on the Hong Kong Stock Exchange at the end of 2025, has announced another significant achievement. Following a successful start to its business development (BD) activities in 2026, the company this week signed a research and development collaboration agreement worth over 500 million HKD with Shenzhen Hengtai Biotech. The partnership focuses on the global development of Insilico's neuroscience innovative drug, ISM8969, exploring indications for central nervous system (CNS) disorders, including Parkinson's disease. Public information indicates that Shenzhen Hengtai Biotech was jointly incubated by Shenzhen Pengfu Fund and Fosun Pharma. This collaboration, to some extent, continues and deepens the cooperative relationship between Insilico Medicine and companies within the Fosun Pharma ecosystem.

Central nervous system (CNS) diseases have long been considered a "hard nut to crack" in the medical field, with difficulties concentrated in two main areas. Firstly, the pathogenesis is highly complex, involving imbalances in neuronal function, abnormal protein aggregation, and the intertwined dysregulation of multiple signaling pathways. Secondly, the presence of the blood-brain barrier, a "natural defense line," prevents the vast majority of drugs from effectively reaching lesions in the brain, leading to an overall high failure rate, long cycles, and substantial investment in CNS new drug research and development. In the ISM8969 project, Insilico Medicine leveraged its generative chemistry engine, Chemistry42, for molecular design and optimization, aiming to achieve both potent inhibition of the NLRP3 target and the candidate molecule's ability to penetrate the blood-brain barrier under the premise of oral administration. Preclinical data indicate that ISM8969 demonstrates ideal anti-inflammatory activity and a favorable safety profile in various models of neuroinflammation and neurodegenerative diseases, while also achieving the preset levels for brain exposure. This project also serves as a concrete example of using an AI drug discovery platform to design molecules and achieve property optimization in CNS indications, which are characterized by more complex parameters and greater mechanistic uncertainty. It provides a reference case for AI-driven drug discovery to engage in high-difficulty disease areas that have long lacked effective therapies through source innovation, beyond just cost reduction and efficiency gains.

According to the agreement terms, both parties hold 50% of the global rights to this project. Insilico Medicine will lead the Investigational New Drug (IND) application and Phase I clinical trials for ISM8969, after which Hengtai Biotech will be responsible for subsequent clinical development, regulatory submissions, and commercialization. For Insilico Medicine, this division of labor helps it concentrate resources on early-stage R&D and platform capability iteration. For Hengtai Biotech and the Fosun Pharma system behind it, this adds an innovative pipeline asset targeting neuroinflammation and neurodegenerative diseases to their portfolio.

The ability to deliver results and generalize applications is a key aspect of AI drug discovery. As early as 2022, Insilico Medicine and Fosun Pharma established one of the larger AI drug discovery strategic collaborations in the Asia-Pacific region at that time, involving a $13 million upfront payment and up to $82 million in milestone payments. The collaboration included AI-driven anti-tumor drug discovery projects around four specified targets, as well as the co-development of Insilico's internally developed anti-tumor project targeting QPCTL. According to disclosed progress, the candidate molecule for the QPCTL project, ISM8207, received implied permission to enter clinical trials from the Center for Drug Evaluation (CDE) of the National Medical Products Administration (NMPA) in July 2023, completed the first patient dosing in May 2024, and is currently in Phase I clinical trials. Regarding multi-target AI drug discovery, Insilico Medicine has delivered preclinical candidate molecules to Fosun Pharma, triggering corresponding milestone payments.

Unlike some traditional biotechs that focus on a single disease area, since launching its first pipeline in 2021, Insilico Medicine's pipeline strategy over the past five years has continuously expanded its scope. It has gradually broadened from initially focusing mainly on fibrosis, immunology, and oncology to include multiple therapeutic directions such as metabolic diseases and neuroscience. This evolution is related to the positioning of its Pharma.AI platform, which emphasizes project screening and decision-making based on large-scale data and systematic analysis, relatively reducing reliance on the research background of any single team or individual. With continuously updated algorithms and data foundations, this relatively "domain-agnostic" approach reserves space for expansion into more indications.

In the biopharmaceutical industry, incremental collaborations extending from existing cooperative achievements are often regarded as a reference indicator for assessing the stability and sustainability of a technology platform. This renewed collaboration with a company related to Fosun Pharma is not the first time Insilico Medicine has expanded the scope of cooperation based on an existing strategic partnership. Reviewing Insilico's BD progress in recent years, the Italian multinational pharmaceutical company Menarini consecutively introduced its early-stage AI drug R&D pipelines with a total deal value exceeding $500 million over two years. Subsequently, Eli Lilly evolved from an initial software platform collaborator to a strategic partner in drug discovery and development. The continuous deepening of these collaborative relationships undoubtedly also reflects the market's growing recognition of Insilico Medicine's ability to translate its technological advantages into deliverable, tangible assets.

Since entering the fast track for its listing around December 2025, Insilico Medicine has significantly accelerated its BD activities. In mid-December 2025, the company licensed the Greater China rights for its renal anemia pipeline asset ISM4808, which had already received clinical trial approval, to Taiwan's Taigen Biotechnology. Then, at the beginning of this year, it disclosed that its collaborative pipeline with Hisun Pharmaceutical had achieved a stage milestone. Shortly after, it kicked off its 2026 global BD transactions with a strategic collaboration totaling up to $888 million, entering into a multi-year R&D agreement with the multinational pharmaceutical company Servier for an oncology project. The over 500 million HKD co-development agreement with Hengtai Biotech announced this week further complements its collaborative footprint in the neuroscience field. Judging by the frequency of deals and individual transaction amounts, this series of collaborations reflects, to some extent, the increasing efficiency of connecting Insilico's pipeline assets with different types of partners across various regions, as well as the growing attention they are receiving.

From a business structure perspective, Insilico Medicine, centered around its proprietary Pharma.AI platform, has formed a three-tiered structure: "Platform Empowerment + Internal Pipeline Development + External Business Development." On one hand, it provides AI-powered drug discovery decision-making tools to large pharmaceutical companies and biotechs through the Pharma.AI platform and its sub-modules, generating platform subscription fees and technology service revenue. On the other hand, it utilizes the same platform to internally incubate a batch of its own innovative pipelines, rapidly building an asset pool "from target to PCC" in disease areas such as fibrosis, oncology, immunology, and metabolism. Building on this foundation, it externally licenses out some assets or capabilities through regional or global out-licensing, co-development, and early-stage discovery collaborations, realizing a continuous cash flow from upfront payments, R&D and commercial milestones, and subsequent sales royalties.

Under the current model, Insilico Medicine's structural advantage lies in its capability for "batch, rapid production of Pre-IND / PCC-level assets." If the front-end asset generation capability and the back-end BD conversion ability form a virtuous cycle, it theoretically has the potential to establish a closed loop of "Platform → Pipeline → Transaction → Reinvestment into Platform," achieving the leap for AI drug discovery from a technological platform to a sustainable business model. More importantly, Insilico's pathway also provides a replicable paradigm for the entire AI drug discovery sector. In the context where traditional AI biotechs generally rely on long-term heavy asset investment, have extremely long payback cycles for internally developed pipelines, and possess weak "self-sustaining" capability in the early stages, Insilico Medicine has implemented a forward-looking strategy combining "platform revenue + pipeline licensing + strategic collaboration." This has enabled the company to achieve a partial self-circulating cash flow through multi-layered BD activities even before generating large-scale product sales. This model, which accelerates monetization through technology and asset output in the early R&D stages and uses rolling transactions to feed back into platform iteration and pipeline expansion, offers a commercially viable template for AI biotech companies to escape the traditional dilemma of "burning cash long-term while struggling to achieve profitability short-term."

In conclusion, as AI-driven pharmaceutical companies like Insilico Medicine continue to increase their collaborative projects, external scrutiny of their true clinical and commercial translation capabilities is rising in parallel. For AI drug discovery, compared to model performance and computing scale that are difficult for the average person to perceive, the more critical metrics are the ability to deliver clinically meaningful results in specific projects and to translate those projects into sustainable commercial returns in the real world. Simultaneously, against the backdrop of an ever-expanding collaboration network, maintaining the pace of technological iteration at the platform level during frequent engagements with external partners, and ensuring that each round of collaboration feeds back into the algorithms, data, and pipeline asset pool—rather than dissipating advantages amidst fragmented demands—will test the internal R&D management and global coordination capabilities of AI-driven companies. Every BD deal finalized today is both an extension of the commercialization path for AI drug discovery and a footnote to the overall narrative of this emerging field. Whether AI drug discovery can deliver convincing clinical and market results in these specific projects remains to be seen over time. The real answers will likely only gradually emerge with the next batch of clinical data, the next round of collaboration deals, and even the next generational upgrade in technology.