Zheshang Securities has released a research report indicating that the pharmaceutical R&D industry is undergoing a similar industrial transformation: the number of biochemical large models is growing explosively, and foundational biological models have achieved significant capability improvements through multiple iterations. The development logic of AI-driven drug discovery is transitioning from scientist-driven decision-making to a new phase of intelligent autonomous decision validation, potentially reshaping the scientific research paradigm and enhancing platform value. In the long term, the firm recommends focusing on R&D platforms capable of continuous iteration.

Industry stage

AI-driven drug discovery has evolved from computational chemistry to intelligent development. Since the 1960s, the impact of computer science on the pharmaceutical sector has progressed through the computational chemistry era, the high-throughput automation era, the machine learning era, and finally the intelligent agent era. The industry has also transitioned technologically from early computational chemistry, simulation, structure prediction, and molecular generation to automation.

Implementation pace

The development of AI drug discovery technology mirrors the advancement of general AI capabilities in the pharmaceutical sector. The firm notes that general AI technology has experienced rapid improvements in large language model capabilities, followed by intelligent agent products such as ClaudeCode and Workbuddy, which have genuinely transformed workflows and outcomes in certain industries. The firm observes that the pharmaceutical R&D industry is undergoing a similar transformation: the number of biochemical large models is surging, and foundational biological models represented by AlphaFold have undergone multiple iterations, achieving substantial capability enhancements. Their functionality has evolved from task-specific predictions to simultaneously predicting complex structures of all types of biomolecules, including proteins, nucleic acids, small molecules, ions, and modified residues, with significantly improved accuracy. Most of these foundational models are open-source.

Paradigm shift

Early-stage drug discovery automation platforms have entered the validation and implementation phase. The closed-loop system of multi-agent combined with wet-dry laboratory experiments forms an AI drug discovery platform, which essentially automates preclinical drug R&D and validation. Through full-process task orchestration and autonomous experimental design, the scientific research workflow, traditionally led and decided by scientists, is being converted into an intelligent agent-led process for the entire early-stage drug discovery. This shifts early drug discovery from a low-frequency validation paradigm to a new high-frequency, high-throughput, multi-round rapid iteration model. While pipelines developed by new automation platforms have not yet entered the clinical validation stage, the large-scale collaboration agreements with multinational corporations and the explosive performance of companies such as Twist and GenScript in the United States indicate that this paradigm shift has moved from theoretical research into the validation and implementation phase.

Industry outlook

The platform iteration process benefits the entire industry chain, resulting in the capability explosion and barrier establishment of AI drug discovery platforms. The firm believes that from an industry development perspective, high-frequency iteration will lead to a surge in demand in the validation phase in the short term. However, in the long run, rapid iteration may bring about platform capability enhancement and generalization. The continuous improvement of discovery platform capabilities may become the core of the industry, and the "Claude moment" for the pharmaceutical industry may be approaching.

In the short term, the firm recommends focusing on: 1) Gene synthesis: GenScript Biotech, Sino Biological, ACROBiosystems, GeneUniversal; 2) Protein expression and purification: Shanghai OPM Biosciences, Nanomicro Technology, Sepax Technologies; 3) Recombinant target proteins/cytokines: ACROBiosystems, Novoprotein, Sino Biological, Hycell, Vazyme, UBiQ; 4) Small molecule synthesis: Bide Pharmatech, Hycell, HaoFen Biotech, Aladdin, Titan Technology; 5) Mouse models: Biocytogen, GemPharmatech, Shanghai Model Organisms; 6) Preclinical CRO: WuXi AppTec, Pharmaron, WuXi Biologics, WuXi XDC, Joinn Laboratories, InnoStar, Medicilon.

In the long term, the firm recommends focusing on continuously evolving R&D platforms: XtalPi, InSilico Medicine, Jietai Technology, Hualan Biological, among others.

Risk warnings

AI drug discovery R&D progress may fall short of expectations; industry competition may intensify; clinical trial results may underperform expectations; and international relations tensions may lead to slower-than-expected cross-border collaboration progress.