Zheyuan Technology After the AI Drug Discovery Sector Returns to Rationality: Evaluation of Commercial Prospects and Investment Value

Currently, the AI drug discovery industry is experiencing a critical transition period from capital frenzy to rational development. Data shows that as of December 31, 2024, the number of AI drug discovery companies in China has reached 105, and the industry has entered a pattern of ‘hundreds of boats competing’ [1]. AI drug discovery enterprises have shifted from pure equity financing to diversified financing; for example, XtalPi obtained stable financing channels through listing, while Insilico Medicine used bank loans to reduce equity dilution [1].

The industry as a whole shows three major trends: first, technology commercialization has entered the ‘deep water zone’; second, differentiation in segmented sectors has intensified; third, the attitude of payers has shifted from government procurement-oriented to market-driven [1]. The global AI + medical solutions market size is expected to increase from $13.7 billion in 2022 to $155.3 billion in 2030, with a compound annual growth rate of 35.5% [2].

Zheyuan Technology’s core technology, ‘Life Function Digital Twin’, represents the cutting-edge direction of computational medicine. This technology can build high-precision digital models of disease biological processes, enabling accurate simulation and prediction of drug action mechanisms. In the validation of the Beijing Cancer Hospital project, its prediction results were completely consistent with real clinical trials, which provides strong clinical validation for the reliability of the technology [1].

Compared with traditional AI drug discovery companies, Zheyuan Technology’s technical advantages are reflected in:

• Systematic modeling
  : Not only focusing on molecular design, but also attaching importance to the digital reconstruction of the entire disease process
• Prediction accuracy
  : Achieving precise prediction of clinical results through digital twin technology
• Multi-dimensional integration
  : Integrating multi-dimensional data such as genomics, proteomics, and metabolomics

The ‘AI4S + Disease’ (Artificial Intelligence for Science + Disease) model reflects the strategic transformation from general AI to deep penetration into vertical fields. The characteristics of this model include:

1. Science-oriented
   : Focusing on understanding disease biological mechanisms as the core, rather than simple algorithm application
2. Vertical deep cultivation
   : Building a professional knowledge system for specific disease fields
3. Closed-loop validation
   : A complete technical closed loop from theoretical prediction to clinical validation

The ‘Innovative Drug IP Factory’ model chosen by Zheyuan Technology has the following characteristics:

• New target discovery authorization fees
• Drug repurposing technical service fees
• Virtual clinical trial prediction services
• Milestone payments for joint development
• Later sales sharing

• Reducing traditional wet experiment investment
• Lowering the risk of clinical trial failure
• Improving R&D efficiency and shortening the R&D cycle

From the perspective of industry benchmarking, the commercialization paths of leading AI drug discovery enterprises have been initially verified:

• 2024 revenue of 266 million yuan, a year-on-year increase of 53%
• In-depth cooperation with international pharmaceutical companies such as Pfizer, with cooperative revenue accounting for 75%
• The platform shortens the R&D cycle by an average of 30% [1]

• Licensed an oncology candidate drug externally for $550 million in January 2025
• Transitioned from a software service model to an AI-driven Biotech development model
• The commercialization model has been initially established [3]

• Completed Series C financing of 400 million yuan in 2024
• 3 candidate molecules entered the IND application stage
• Received a total of $120 million in down payments and milestone payments in 2024 [1]

The industry pain points of drug R&D being ‘a narrow escape from death’ are mainly reflected in:

• Long R&D cycle (average 10-15 years)
• High R&D cost (average $2.6 billion)
• Low success rate (clinical Phase I success rate is about 90%, Phase III drops to 50%)

Zheyuan Technology’s virtual clinical trial technology can provide solutions in the following links:

1. Target validation stage
   : Predict target effectiveness through digital twin to reduce later failure risks
2. Lead compound optimization
   : AI-driven molecular design improves druggability
3. Preclinical prediction
   : Virtual clinical trials simulate human reactions to optimize clinical protocol design

According to industry data, the application effect of AI technology in various links of pharmaceutical manufacturing is significant:

• Target discovery accuracy can reach more than 82%
• Molecular design efficiency increased by 3 times
• R&D cycle shortened by an average of 30% [1][4]

In Zheyuan Technology’s validation project at Beijing Cancer Hospital, the prediction results were completely consistent with real clinical trials. This validation case has high gold content and provides empirical support for the clinical applicability of the technology.

1. Technical barrier
   : Life Function Digital Twin technology builds a high technical threshold
2. Market demand
   : Pharmaceutical companies have an urgent need to reduce costs and increase efficiency, and the market space is huge
3. Business model
   : The IP authorization model has good scalability
4. Validation case
   : Hospital project validation provides credible empirical data

• The generalization ability of the model needs more verification
• There are differences in applicability in different disease fields

• Pharmaceutical companies have a long acceptance cycle for new technologies
• Increased competition may compress profit margins

• Regulatory standards for AI-assisted R&D are still being improved
• Strict requirements for authenticity verification of clinical trial results

Based on industry benchmarking analysis, the following valuation methods are recommended:

1. Technical value method
   : Considering the innovation and verification results of digital twin technology
2. Revenue prediction method
   : Based on the prediction of potential authorization cooperation and service revenue
3. Market comparison method
   : Referring to the valuation level of listed AI drug discovery enterprises

Considering that Zheyuan Technology has completed Series A1 financing and has unique digital twin technology and clinical validation cases, its investment value is relatively prominent in the current market environment where AI drug discovery returns to rationality, but attention still needs to be paid to the progress of subsequent commercial implementation.

Against the background of the AI drug discovery sector returning to rationality, Zheyuan Technology’s ‘Life Function Digital Twin’ technology and ‘AI4S + Disease’ model show unique technical advantages and commercial potential. Virtual clinical trials have important value in solving the pain points of high cost, high risk, and long cycle in drug R&D, and the validation case of Beijing Cancer Hospital provides strong support for the reliability of the technology.

For investors, Zheyuan Technology’s investment logic is clear, but attention still needs to be paid to:

1. Specific progress of subsequent commercial cooperation
2. Technical validation results in more disease fields
3. Changes in regulatory policies affecting AI drug discovery

Overall, in the process of the AI drug discovery industry transitioning from frenzy to rationality, enterprises like Zheyuan Technology with core technical barriers and empirical verification are more likely to have long-term investment value.

[1] 2025 China AI + Medical TOP30 In-depth Analysis: Double Verification of Technical Breakthroughs and Commercial Validation, Jinling AI Data
[2] China Post Securities Research Report “AI + Medicine: Inevitable, the Future Has Arrived”, February 2025
[3] Ten Years of Ups and Downs in AI Drug Discovery: Pipeline Failures, Capital Retreat, and Persistence Before Dawn, iyiou Think Tank
[4] AI Reshapes Biopharmaceuticals: Full-chain Innovation from Target Discovery to Clinical Development, Biopharmaceutical Industry Conference Report
[5] KPMG “Biotech Innovation Top 50” China Enterprise Report, October 2025