Guotai Haitong has released a research report stating that as artificial intelligence becomes increasingly integrated across target discovery, molecule generation, clinical trial design, and post-market data analysis, its adoption rate within the pharmaceutical industry continues to rise. AI-powered drug discovery is evolving into a critical infrastructure for innovative drug R&D. The period from 2025 to 2026 is identified as a key window for multinational corporations (MNCs) to significantly increase their investment in AI. Through strategies such as acquiring foundational model companies, jointly establishing computing power laboratories, and engaging in multi-project platform collaborations, MNCs are accelerating the development of an integrated "computing power-algorithm-data-experimentation" system. With the synergistic advancements in both artificial intelligence and pharmaceuticals, the outlook for AI in drug discovery is viewed positively. The main viewpoints from Guotai Haitong are as follows:
AI is reshaping the drug R&D paradigm, moving the industry from conceptual stages into a period of practical validation. The deep integration of generative models and reinforcement learning has significantly improved the efficiency of molecular design and shortened R&D cycles. As AI permeates all aspects of the drug development process, its industry penetration rate is steadily increasing, solidifying its role as essential infrastructure for new drug innovation.
XTALPI has built a data feedback loop barrier through its "computational simulation + automated experimentation + robotics system" approach. Instead of focusing on its own drug pipelines, XTALPI advances multiple pipelines to the IND and clinical stages via collaboration and incubation models, continuously strengthening its platform capabilities through cross-project validation. Its differentiated advantage lies in its ability to consistently generate high-precision computational data and standardized experimental data, allowing for model iteration based on real-world feedback, which provides a long-term compounding effect.
INSILICO has established a three-tier structure comprising "platform empowerment + proprietary pipelines + external business development," forming an end-to-end closed-loop capability. Leveraging its Pharma.AI platform, INSILICO achieves deep integration in target discovery, molecule design, and clinical prediction. Several of its proprietary pipelines have entered clinical stages, and the company has realized commercial value through substantial out-licensing deals. Its revenue structure is gradually evolving into a dual-engine model of "project-based income + recurring software revenue," indicating a transition of the platform's value from pure technological capability to asset-generating capability.
Multinational pharmaceutical companies are upgrading AI from a point solution to a foundational layer of their R&D and production systems. The 2025-2026 timeframe represents a critical period for MNCs to fully commit to AI. By acquiring foundational AI model companies, co-building computing labs, and pursuing multi-project platform collaborations, they are accelerating the creation of an integrated computing-algorithm-data-experimentation ecosystem. Transaction structures commonly employ a "low upfront payment + high milestone payment" model, which helps manage conversion risks while securing potential technological gains.
Overall, AI is becoming a core variable in the upgrade of drug R&D systems. Given the positive momentum in both AI and pharmaceutical fields, the prospects for AI in drug discovery are favorable. Related companies suggested for attention include INSILICO (03696), XTALPI (02228), VIVA BIOTECH (01873), as well as other relevant players. Risks include the potential for AI technology development to fall short of expectations, BD deals not materializing as anticipated, delays in drug R&D progress, and changes in regulatory requirements.