The innovative medicine sector is gaining significant momentum as artificial intelligence transforms the drug development landscape from discovery through clinical trials and regulatory approval. A landmark development has emerged with the recent market approval of Yisitevir Hydrochloride Tablets (brand name: Aipusiwei), a novel drug jointly developed by Westlake University, Westlake Laboratory, and Westlake Pharmaceutical (Hangzhou) Co., Ltd. This represents China's first original drug approved for market with the assistance of AI in its research and development process.

Professor Huang Jing from Westlake University's School of Life Sciences noted that AI-assisted drug R&D has moved beyond conceptual discussions to genuinely support original medicines from discovery through clinical development to market launch. Aipusiwei stands as Westlake University's first fully self-developed Class 1 innovative drug, designed for treating adults with mild to moderate COVID-19 infections. Class 1 innovative drugs represent the highest tier in China's pharmaceutical registration classification, signifying source innovation from zero to one, distinguished by entirely new chemical structures, novel mechanisms of action, and clear clinical value.

Notably, Aipusiwei is the world's first small molecule innovative drug successfully approved based on DNA-encoded compound library (DEL) technology — achieving a historic breakthrough from zero to one, 34 years after the DEL concept was first introduced. The entire journey from source discovery to completion of clinical trials took only three and a half years.

Analysts point out that traditional pharmaceutical development faces the challenging "double ten" dilemma — ten years of development time and one billion dollars in investment — with historically low success rates. AI technology has now comprehensively penetrated core pharmaceutical processes including target identification, virtual screening, de novo design, ADMET prediction, and automated synthesis. This effectively overcomes the throughput limitations of traditional high-throughput physical blind screening, substantially enhancing pharmaceutical efficiency and success rates.

The global AI pharmaceutical market has grown from $790 million in 2021 to $2.41 billion in 2025, with projections reaching $2.99 billion by 2026. China's AI pharmaceutical market has already surpassed RMB 600 million in 2025. Institutional forecasts suggest the sector could exceed $46 billion by 2035, with annual compound growth rates surpassing 33%. The Chinese market is growing in tandem with global trends and is positioned to become one of the most certain incremental markets.

From an operational data perspective, AI-related business is emerging as a key driver of order growth and revenue expansion for several companies. Meanwhile, downstream pharmaceutical enterprises are actively embracing AI, with R&D efficiency gains becoming increasingly apparent. At the results briefing held on August 27, Insilico Medicine's Co-CEO and Chief Scientific Officer Ren Feng indicated that the company's BD orders have remained active this year, with multiple early-stage AI-empowered R&D projects reaching collaboration or pipeline licensing agreements with partners including Servier, Eli Lilly, and Qilu Pharmaceutical.

Fosun Pharma is also accelerating the integration of AI tools into early drug discovery. At the company's performance briefing, Executive Director and Chairman Chen Yuqing stated that as of the first half of the year, some of the company's early research molecular pipeline development has adopted AI digital intelligence tools, with two AI-assisted generated and structurally optimized new molecules having entered the PCC (Pre-Candidate Compound) stage. According to the interim report, by the end of the first half of 2026, Fosun Pharma had launched more than 25 high-value AI projects with over 50 scenarios advancing concurrently, covering applications across target prediction, molecular optimization, clinical trials, and pharmacovigilance.

CITIC Construction Investment research indicates that AI pharmaceuticals are undergoing rapid iteration and transformation. Algorithm upgrades and enhanced computing power are consolidating the application foundation, with AI widely deployed in target screening, molecular generation, and ADMET prediction, while extending toward macromolecular drug development. Traditional pharmaceutical R&D cycles remain lengthy, costly, and prone to failure, whereas AI-driven increases in dry experiments and rising demand for wet experiments are jointly boosting early-stage drug discovery efficiency and success rates, generating sustained incremental growth for the downstream industrial chain.

Where investors should focus

Several Hong Kong-listed companies are at the forefront of this AI-driven pharmaceutical transformation. Insilico Medicine (03696) has maintained active BD order momentum this year, with multiple AI-empowered early-stage projects securing R&D collaborations or pipeline licenses with industry leaders including Servier, Eli Lilly, and Qilu Pharmaceutical, as highlighted during the August 27 results briefing by Co-CEO and Chief Scientific Officer Ren Feng.

Fosun Pharma (02196) is advancing AI adoption in early drug research, with Chairman Chen Yuqing confirming that AI digital intelligence tools have been deployed in early research molecular pipeline development for certain new drugs, achieving two AI-assisted new molecules entering the PCC stage. The company's interim report shows over 25 high-value AI projects in production and more than 50 scenarios progressing across target prediction, molecular optimization, clinical trials, and pharmacovigilance.

XtalPi (02228) generated revenue of RMB 393.6 million in the first half of this year, with drug discovery solutions contributing approximately RMB 200.1 million and AI for Science intelligent solutions contributing around RMB 193.5 million. The company has established a multimodal AI drug discovery platform covering small molecules, molecular glues, macromolecules, peptides, and small nucleic acids, while building a full-stack AI4S R&D system integrating scientific agents, proprietary models, robotic experimentation, and proprietary data feedback. These capabilities have achieved scaled commercial deployment and continue to convert into R&D pipelines and high-value drug assets.