Southwest Securities has released a research report indicating that the AI pharmaceutical industry has progressed through its nascent and developmental stages, entering a phase of mature commercialization validation since 2024. The core competitive focus has shifted from model algorithms to high-quality data, closed-loop experimental capabilities, and the ability to commercialize AI-driven drugs.
AI pharmaceuticals are currently at a critical juncture for commercial value validation, and the report suggests investors seize opportunities across three key dimensions: research and development, commercialization, and business development. The brokerage recommends focusing on three main investment themes: technological barriers, execution progress, and earnings elasticity.
How does AI empower the entire drug R&D process?
AI applications have evolved into a full-chain productivity tool covering drug discovery, clinical development, commercialization, and manufacturing supply. Its core value lies in cost reduction and efficiency gains: the drug discovery phase can save 70%-90% of time, preclinical and clinical stages can save 50%-80% and 50%-60% respectively, and cumulative R&D costs can be reduced by approximately 50%.
How does AI enhance specific segments of drug R&D?
In target discovery, knowledge graphs combined with multi-omics integration help overcome the bottleneck of undruggable targets. Virtual screening leverages machine learning to enable rapid pre-screening of billion-compound libraries in seconds. Molecular generation has entered the era of 3D diffusion models, directly generating suitable molecules within target spaces. ADMET prediction enables early druggability screening, while automated DMTA closed loops connect virtual design with physical synthesis. On the clinical side, intelligent patient matching and adaptive trial design address the pain points of difficult enrollment and high costs. AI now covers the R&D optimization of all drug categories, including small molecules, antibodies, small nucleic acids, mRNA, and CAR-T therapies.
What constitutes the core competitive moat in AI pharmaceuticals?
The industry has established three primary barriers: computing power, data, and models, with data and models carrying greater importance than computing power. A dry-wet lab closed loop represents the ultimate high-level barrier that integrates all three elements.
What is the investment logic for AI pharmaceuticals?
Given that the sector is in a critical phase of commercial value validation, the report recommends positioning across R&D, commercialization, and BD dimensions. On the R&D front, investors should monitor core technology breakthroughs and clinical data disclosure milestones, particularly Phase II/III druggability validation and data catalysts from academic conferences. Companies with dry-wet closed-loop capabilities will establish more durable long-term barriers. Along the industry chain, upstream beneficiaries—those supplying wet lab services or tools—stand to gain from rising demand and are core beneficiaries during the validation period. In BD activity, global pharmaceutical companies continue active partnerships with AI platforms, with major collaborations and license-out deals serving as important share price catalysts.
Potential investment targets
Based on the three main themes of technological barriers, execution progress, and earnings elasticity, the report highlights the following directions and related companies:
1) Full-stack platform leaders with prominent dry-wet closed-loop capabilities: companies with end-to-end AI R&D capabilities and proprietary automated wet lab systems have the deepest long-term barriers, such as Insilico Medicine and XtalPi Holdings.
2) Beneficiaries across the AI industry chain with earnings delivery certainty: CXO companies integrating AI technology upgrades are well-positioned to capture industry demand first, including CRO+AI platforms like Chengdu Leadisc, Hongbo Medical, and Hualan Biological; comprehensive CXO players WuXi AppTec and Pharmaron; CDMO providers Asymchem, WuXi Biologics, and Porton Pharma; clinical CRO Tigermed; and upstream sequencing, reagent, and consumable chains including GenScript Biotech and GeneUniversal for gene synthesis, Vazyme for reagents and consumables, AUPIMA for serum culture media, Bio-Techne for recombinant protein reagents, and Biocytogen and Nanmol Bioscience for model animals.
3) Leaders in differentiated technology tracks with scarce niche targets: focusing on AI-driven drug formulation and nanodelivery, represented by Jietai Technology.
4) Domestic pharma leaders with self-built AIDD platforms, using AI to accelerate internal drug pipeline iteration: Hengrui Medicine, Sino Biopharmaceutical, Fosun Pharma, and CSPC Pharmaceutical Group.
Risk warnings
Potential risks include slower-than-expected AI technology iteration, failure risks in innovative drug R&D, clinical progress falling short of expectations, changes in industry regulatory policies, and fluctuations in computing power supply.
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