Recently , Insilicon announced a strategic collaboration with Qilu Pharmaceutical Group and its subsidiary, Shanghai Qilu Pharmaceutical Research Center, on drug development worth over HK$931 million .

 

According to the agreement, the two parties will rely on Insilicon's Pharma.AI platform to develop small molecule inhibitors for cardiovascular and metabolic diseases targeting specific targets. Insilicon will be responsible for the design and optimization of small molecule drugs, while Qilu Pharmaceutical will be responsible for subsequent development and commercialization.

 

It is understood that Insilicon was listed in Hong Kong on December 30, 2025, less than a month ago. This cooperation with Qilu Pharmaceutical is the fourth cooperation that has been implemented since its listing.

 

01 Reshaping the R&D Paradigm

 

In the pharmaceutical industry, traditional new drug development is characterized by high investment, high risk, and long cycles. There is a saying in the industry that "the 'double ten rule'" applies to innovative drugs. From the initial research and development project to the final successful launch, it takes an average of ten years and billions of dollars to invest, while the probability of successfully passing clinical trials and achieving commercialization is less than 10%. This inefficient and costly research and development model has severely restricted the progress of global innovative drug development.

 

With the rapid iteration and breakthroughs in artificial intelligence (AI) technology, this inherent dilemma is being gradually rewritten. AI technology, with its powerful data processing, pattern recognition, and prediction capabilities, can significantly improve efficiency at every stage, from target discovery and molecular design to clinical outcome prediction, bringing new opportunities for new drug development.

 

Taking InSilicon's self-developed Pharma.AI platform as an example, it consists of Biology42, Chemistry42, Medicine42, and Science42 , aiming to cover the entire drug discovery and development process. This platform can identify new drug targets, design molecular compounds de novo for new or known targets, and optimize the clinical development pathway of candidate drugs.

 

According to data disclosed by InSilicon, with the help of Pharma.AI, the average time from target discovery to nomination of preclinical candidate compounds (PCC) for its self-developed projects has been reduced to 12 to 18 months . In comparison, traditional early-stage drug development methods take an average of 4.5 years to complete this process, resulting in a significant improvement in efficiency.

 

At the same time, each project only needs to synthesize and test 60-200 molecules , which is far less than the testing of hundreds or even thousands of molecules in the traditional R&D model. This greatly shortens the R&D cycle and achieves a double saving of time and material costs.

 

Rentosertib (ISM001-055) , a novel anti-idiopathic pulmonary fibrosis candidate drug discovered by AI. This is InSilicon's fastest-progressing self-developed pipeline. This project is the world's first AI-driven drug to achieve positive results in a Phase IIa clinical trial. InSilicon completed the early drug discovery work of the project in just 18 months , screening and testing 78 molecules before successfully identifying the candidate compound. The time from PCC to Phase I clinical trial was only 9 months, clearly demonstrating the "accelerated" nature of AI-enabled R&D.

 

Leveraging the Pharma.AI platform, Insilicon has built a product pipeline of over 30 projects , covering multiple therapeutic areas including fibrosis, oncology, immunology, cardiovascular and metabolic diseases, and central nervous system disorders. In addition to Rentosertib, in the oncology field, Insilicon's independently developed pan-TEAD inhibitor ISM6331 and MAT2A inhibitor ISM3412 have both initiated global multicenter Phase I clinical trials.

While advancing its self-developed pipeline, Insilicon is also commercializing its Pharma.AI platform technology through software licensing and joint R&D collaborations .

 

According to its prospectus, Insilicon has previously entered into three pipeline licensing collaborations with companies such as Exelixis and Stemline, with a total value of up to $2.1 billion . In addition, Insilicon has also established joint R&D collaborations with pharmaceutical companies such as Sanofi, Eli Lilly, and Fosun Pharma, and target discovery collaborations with pharmaceutical companies such as Novo Nordisk, Boehringer Ingelheim, and Pfizer.

 

Insilicon successfully listed on the Hong Kong Stock Exchange on December 30, 2025. Within a month of listing, it has reached a number of major cooperation agreements.

 

For example, on January 5, 2026, a research and development collaboration totaling $888 million was reached with Servier, focusing on the discovery and development of innovative anti-tumor therapies. On January 20, a co-development collaboration agreement was reached with Hengtai Bio for the ISM8969 project, accelerating the global development of this novel NLRP3 inhibitor with the ability to cross the blood-brain barrier.

 

This collaboration with Qilu Pharmaceutical further deepens Insil Intelligence's presence in the cardiovascular and metabolic disease field . Insil Intelligence previously announced its proprietary cardiovascular and metabolic disease pipeline at the BIO-Europe conference, encompassing eight drug candidates ranging from seed compounds to IND-enabling stages, with the entire development process driven by Pharma.AI.

 

02 Landing on the "Golden Track"

 

Technological advantages must resonate with the urgent needs of the market to unleash their maximum commercial value. The collaboration between Insilicon and Qilu Pharmaceutical, focusing on cardiovascular and metabolic diseases , perfectly embodies this logic.

 

Cardiovascular and metabolic diseases (such as hypertension, atherosclerosis, diabetes, and their complications) constitute one of the heaviest disease burdens globally. In China, the number of patients with these diseases is in the hundreds of millions, and the prevalence continues to rise due to population aging and lifestyle changes. These chronic diseases require long-term management, but existing therapies still have many unmet needs in terms of efficacy, safety, and ease of use, which provides a vast market space for innovative drug development.

 

In recent years, metabolic disease therapies, represented by GLP-1 receptor agonists, have achieved tremendous success , demonstrating remarkable efficacy not only in lowering blood sugar and weight loss but also in breakthroughs in areas such as cardiovascular protection, significantly changing the treatment landscape for metabolic and cardiovascular diseases. The success of blockbuster drugs such as semaglutide and telpoxetine has not only demonstrated the enormous market potential in the field of metabolic diseases but has also attracted global pharmaceutical giants to re-increase their R&D investment in cardiovascular and metabolic diseases, sparking a new wave of research and development.

 

Amid this trend, the introduction of AI technology appears timely, as it can accelerate the research and development of innovative therapies and help companies gain a competitive edge in the fierce market competition.

 

In recent years, the National Reimbursement Drug List has been dynamically adjusted, and more and more innovative drugs with high clinical value have been included. In particular, since 2025, many blockbuster innovative drugs in the field of cardiovascular metabolism (such as GLP-1 receptor agonists and PCSK9 inhibitors) have entered the reimbursement drug list with significant price reductions through national negotiations. This has improved drug accessibility and reshaped the market competition landscape. This means that in the future, the competitiveness of pharmaceutical companies will increasingly depend on their ability to develop next-generation therapies with clinical differentiation.

 

For leading domestic companies like Qilu Pharmaceutical, partnering with Insil Intelligence is precisely about planning for the future, leveraging AI technology to discover new targets, develop differentiated pipelines, and build pipeline barriers.

 

On the other hand, the pathogenesis of cardiovascular and metabolic diseases is complex, involving the interaction of multiple genes, pathways, and organs. AI technology, especially multimodal generative AI, can integrate massive amounts of heterogeneous information such as genomics, proteomics, transcriptomics, and clinical data to uncover common networks and novel targets hidden behind diseases, and is expected to give rise to groundbreaking innovative drugs.

 

03  Sobering Thoughts Behind Prosperity

 

Recently, the field of AI-driven pharmaceutical manufacturing has seen a flurry of activity, attracting widespread attention both within and outside the industry.

 

In terms of clinical progress , several AI pharmaceutical companies, both domestically and internationally, have announced that their AI-designed products have entered Phase III clinical trials.

 

For example, in December 2025, Derui Pharmaceuticals announced the initiation of a Phase III clinical trial in China for its small molecule GLP-1 receptor agonist MDR-001. At the end of 2025, Generate Biomedicines, a US-based company, initiated a Phase III clinical trial for its antibody drug GB-0895.

 

In terms of industrial cooperation , at the start of 2026, a series of major collaborations were launched globally.

 

On January 5, 2026, Sanofi and Earendil Labs, a subsidiary of Huashen Intelligent Pharmaceuticals, reached a strategic collaboration of up to $2.56 billion, focusing on AI-driven innovative drug development. In January 2026, tech giants Nvidia and Eli Lilly announced a joint investment of $1 billion over the next five years to establish an AI-powered drug laboratory, deepening the application of AI in drug development. XtalPi Holdings has also been active recently, with its subsidiary XtalPi Technology reaching a global strategic cooperation and platform licensing agreement with Gan & Lee Pharmaceuticals for AI-driven peptide innovative drug development.

 

However, behind the high enthusiasm of the capital market and the continuous warming of industry cooperation, AI pharmaceutical companies still face many challenges that need to be addressed, such as the dilemma of commercialization.

 

Taking XtalPi as an example, its revenue reached 266 million yuan in 2024, and the company achieved profitability for the first time in the first half of 2025.

 

According to its prospectus, while Insilicon's revenue grew rapidly from 2022 to 2024 (from US$30.147 million to US$85.834 million), its cumulative net loss over the three years reached approximately US$450 million. The vast majority of its revenue came from drug discovery collaborations and software licensing, while the commercialization revenue from its self-developed pipeline has not yet been realized.

 

The core assets of AI-driven pharmaceutical companies are their technology platforms and R&D pipelines. However, the clinical advancement and commercialization of these pipelines require lengthy cycles and high costs, making it difficult for most AI-driven pharmaceutical companies to achieve profitability in their early stages. In its prospectus, Insilicon also explicitly stated that its future commercial success will largely depend on the technological capabilities of its Pharma.AI platform and whether its drug candidates can be successfully developed and ultimately commercialized using this platform.

 

In addition to commercialization challenges, data quality and standardization issues are also among the challenges facing the AI pharmaceutical industry.

 

Currently, high-quality, standardized biomedical data available for training remains scarce, and data from different sources often exhibits inconsistent standards, creating "data silos" that severely restrict the generalization ability of AI models and their ability to explore unknown areas. How to acquire sufficient high-quality data and establish unified data standards and sharing mechanisms are long-term issues that the AI-driven pharmaceutical industry needs to explore and resolve.

 

Despite numerous challenges, it is undeniable that the AI-driven pharmaceutical industry still has vast market space and development potential.

 

According to data from the Boston Consulting Group (BCG), AI technology can save at least 25% to 50% of time and costs in the preclinical drug discovery process, significantly improving R&D efficiency.

 

According to the latest data from The Business Research Company, the global AI-driven pharmaceutical market will grow from $1.58 billion in 2023 to $5.62 billion in 2028, with a compound annual growth rate of 28.5%, demonstrating strong growth momentum.

 

04 Conclusion

 

For China's pharmaceutical industry, embracing AI seems to have become a "must-take course" concerning future competitiveness. With the emergence of more similar collaborations, a new Chinese pharmaceutical R&D ecosystem empowered by artificial intelligence and more innovative is rapidly taking shape.

 

By editor