Guotai Haitong: AI Pharma Gains Momentum as Multinational Pharma Accelerates Strategic Layout

According to the ZhiTong Finance APP, Guotai Haitong released a research report stating that with the comprehensive penetration of AI in target discovery, molecule generation, clinical design, and post-marketing data analysis, the industry penetration rate continues to increase. AI + pharmaceuticals is becoming an important infrastructure for innovative drug development. The period from 2025 to 2026 will be a critical window for multinational corporations (MNCs) to fully invest in AI. By acquiring basic model companies, co-building computing power laboratories, and engaging in multi-project platform collaborations, they are accelerating the construction of an integrated "computing power-algorithm-data-experiment" system. With the synergy between artificial intelligence and pharmaceuticals, the prospects for AI + pharmaceuticals are promising.

The main viewpoints of Cathay Health are as follows:

AI + Drug Development Reshapes R&D Paradigm, Industry Moves from Concept to Efficacy Validation Phase

The deep integration of generative models with reinforcement learning significantly enhances molecular design efficiency and shortens the R&D cycle. As AI becomes deeply integrated into target discovery, molecule generation, clinical trial design, and post-market data analysis, the industry penetration rate continues to rise, making AI-driven pharmaceuticals a crucial infrastructure for innovative drug development.

XtalPi Builds Data Closed-Loop Barrier with "Computational Simulation + Automated Experimentation + Robotic Systems"

XtalPi does not focus on its own pipeline but promotes multiple pipelines to enter the IND and clinical stages through collaboration and incubation models. The platform's capabilities are continuously strengthened through cross-project validation. Its differentiated advantage lies in the continuous generation of high-precision computational data and standardized experimental data, enabling model iteration based on real-world feedback and demonstrating long-term compounding characteristics.

Insilico Medicine Builds a Three-Layer Structure of "Platform Empowerment + Self-Developed Pipeline + External BD" to Form End-to-End Closed-Loop Capabilities

Relying on the Pharma.AI platform, Insilico Medicine has achieved deep integration in target discovery, molecular design, and clinical prediction. Multiple self-developed pipelines have entered the clinical stage, and commercialization has been realized through large-scale external licensing. The company's revenue structure is gradually forming a dual-engine model of "project-based + recurring software income," and the platform's value is transitioning from technical capabilities to asset capabilities.

Multinational pharmaceutical companies are upgrading AI from a single-point tool to the underlying infrastructure of R&D and production systems.

2025-2026: A Key Window for MNCs to Fully Invest in AIThrough methods such as acquiring foundational model companies, co-building computing power laboratories, and multi-project platform collaborations, this period will accelerate the construction of an integrated "computing power-algorithm-data-experiment" system. The transaction structures generally adopt a "low upfront payment + high milestone-based payments" model, which controls transformation risks while securing potential technological dividends. Overall, AI is becoming a core variable in upgrading drug R&D systems. With the synergy of artificial intelligence and pharmaceuticals, the prospects for AI + drug development are promising.

Relevant Targets

It is recommended to focus on Insilico Medicine (03696), XtalPi (02228), Viva Biotech (01873), HitGen (688222.SH), and Pharmaron (301230.SZ).

Risk Warning

Risk of AI technology development falling short of expectations, risk of BD deals not meeting expectations, risk of drug R&D progress falling short of expectations, risk of changes in regulatory requirements.