Shenwan Hongyuan has released a research report noting that, according to data, the global AIDD market grew from US$5.4 billion in 2019 to US$11.9 billion in 2023, and is projected to reach US$74.6 billion by 2032, with a CAGR of about 22.6% from 2023 to 2032.
Compared with small molecules, large-molecule AI pipelines are less mature, revealing a value-depression landscape.
Wet-lab validation has become the rate-limiting step in model development, and related targets are worth watching.
Main views from Shenwan Hongyuan
Tech giants are entering AI4S through a "computing power + large models + wet-lab closed loop" approach.
According to Frost & Sullivan data, the global AIDD market grew from US$5.4 billion in 2019 to US$11.9 billion in 2023, and is expected to reach US$74.6 billion by 2032, with a CAGR of about 22.6% from 2023 to 2032.
On the policy side, AI4S was listed by the State Council as the first item under "accelerating the implementation of key actions," and the 15th Five-Year Plan for the development of the pharmaceutical industry further clarified the AI-driven transformation of R&D and production paradigms.
Against this backdrop, tech giants are accelerating their entry.
Among tech giants, Anthropic launched Claude for Life Sciences and built its own wet lab; Isomorphic Labs built IsoDDE based on AlphaFold3 and has formed partnerships with Eli Lilly, Novartis, and Johnson & Johnson; domestically, BioMap's xTrimo V4 and Xinsheng Laboratory's AnewFold are also catching up quickly.
Tech giants choose large-molecule models as a breakthrough for multiple reasons
First, the "underlying infrastructure" for small-molecule AI is already relatively mature, and the "model layer" and "data layer" for small-molecule generation and structure prediction are no longer scarce resources.
Second, small-molecule patent barriers are strong and difficult to circumvent, while large molecules such as antibodies still have considerable room for avoidance through differentiated CDR sequences and epitopes.
Finally, compared with small molecules, large-molecule AI pipelines are less mature, revealing a value-depression landscape.
At present, AI drug clinical pipelines are still mainly small molecules, with large-molecule pipelines accounting for only 28%.
Moreover, large-molecule AI clinical pipelines are mainly concentrated in Phase I/II (82%), with only Generate's TSLP monoclonal antibody GB-0895 entering Phase III.
However, this antibody is not a completely new antibody scaffold designed from scratch by AI, but rather one optimized and engineered through AI.
Wet-lab validation becomes the rate-limiting step for large-molecule AI model development, and upstream "water sellers/shovel sellers" will benefit directly
Large-molecule AI models face structural difficulties such as large molecular weight, long sequences, heterogeneous data, and slow DBTL cycles, making wet-lab validation the rate-limiting step in model development.
After large-scale candidate generation becomes routine, conversion efficiency depends on the ceiling of wet-lab throughput, and upstream "water sellers/shovel sellers" will benefit directly.
Among them, Twist guides that AI-related orders will achieve triple-digit growth in FY2026; GenScript's AIDD-related business doubled year on year in 2026H1.
The bank suggests paying attention to related targets, including Twist, GenScript Biotech, ACROBiosystems, and Novoprotein.
Risk warnings: risk that demand for wet-lab validation falls short of expectations, risk that technological iteration falls short of expectations, and risk that model commercialization falls short of expectations.
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