According to Zhitong Finance APP, Orient Securities Company Limited (ASX: 600958) has released a research report stating that with the accumulation of factors such as models, data, and computing power, AI is expected to become a new infrastructure for the global innovative drug industry in the future, disrupting the "anti-Moore's Law" of long cycles, high costs, and high failure rates in innovative drug R&D.
Specifically, frontier fields such as automated wet labs and virtual cells have become the focus of overseas large model giants, and domestic AI pharmaceutical companies have already taken the lead in deploying related facilities, possessing global competitiveness. At the same time, forward-looking indicators such as pipelines and financing of global AI pharmaceutical companies continue to improve. As an indispensable part of the global innovative drug industry chain, domestic life science upstream and CXO sectors are also expected to fully benefit from the entirely new incremental demand brought by AI. The main viewpoints of Orient Securities are as follows:
Competing in Protein Design, the Rise of Automated Wet Labs
In recent years, models represented by AlphaFold and Claude have achieved technological breakthroughs in the field of protein design, providing important tools for early-stage structural design and screening of drugs, generating substantial demand for wet experiments such as protein synthesis and affinity testing. Considering that existing biological data is far from meeting the requirements of AI4S for data scale and quality, as the paradigm shifts from using AI to analyze existing biological data to organized, large-scale production of high-quality datasets, AI will participate in and accelerate the production of biological knowledge. Therefore, automated wet labs capable of producing high-quality data at high throughput and low cost will be scarce resources and core barriers in the AI4S era. As AI applications extend to the entire chain of new drug R&D, this will drive a leap in related demand.
Betting on Virtual Cells, Opening a New Infrastructure for AI Drug Discovery
The real bottleneck in new drug R&D lies in finding a biological mechanism whose intervention can alter the disease progression of patients. AI represented by virtual cells and virtual humans is expected to solve this problem. Specifically, a virtual human extracts signals from specialized data using dozens of professional models and converts them into a form understandable by general models, then an inference layer connects and integrates these insights into mechanisms and targets. AI identifies mechanisms that hold true in both human and cellular data, achieving a correct closed loop in drug development, and becomes stronger with iteration, ultimately bringing about a systematic restructuring of R&D productivity, transforming drug discovery from a craft highly dependent on scientists' personal intuition into a scalable, iterable, and predictable industrial process. Recently, large model giants such as Google and Meta, together with US government departments, announced an investment of 1.8 billion US dollars in the virtual cell field, which is expected to initiate a new global infrastructure for AI drug discovery.
Financing and Pipelines Improving, Upstream and CXO to Benefit
Financing and pipelines are important forward-looking indicators for demand in the life science upstream and CXO industries. From a financing perspective, in 26Q2, venture capital financing for AI pharmaceutical companies reached 5 billion US dollars, already accounting for about one-third of healthcare industry financing, but only about one-thirtieth of AI company financing. AI pharmaceutical financing has become an important source of funds for global new drug R&D and still has significant room for growth. From a pipeline perspective, some companies such as Insilico Medicine have made rapid pipeline progress, expected to continuously read out key clinical data over the next four years, bringing important catalysts to the industry. With the advancement of pipelines under research and the diffusion of demand for automated wet labs and other needs across the entire new drug R&D chain, domestic life science upstream and CXO companies will also benefit from the incremental demand brought by AI drug discovery, with high prosperity rising further. Related targets: Insilico Medicine, GenScript Biotech, ACROBiosystems, Joinn Laboratories, Innostellar, WuXi AppTec, Jitai Technology-P. Risk warnings: data, technology, clinical, regulatory, and geopolitical risks.
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