The Zhitong Finance App learned that Zhang Yidong of Haitong International released a research report saying that innovative drugs are expected to become one of the directions with great growth potential and valuation flexibility in the AI application stage. The peculiarity is that the value brought by AI may far exceed current revenue and profit improvements. For most industries, AI mainly reflects value through efficiency improvements and profit growth in existing businesses; the value of innovative pharmaceutical companies depends on the potential commercialization value of the research pipeline, and their valuation depends not only on current profits, but also on factors such as expected future drug sales spikes, contribution to cash flow, and fulfillment time. Improving the efficiency of AI R&D is expected to improve the input-output ratio of existing projects, and may improve the cash flow conditions; large-scale drug discovery is expected to expand potential R&D assets and commercialization space, and drive forward long-term growth expectations. The latter need not shorten the long-term cash flow period, but it may increase the expected value of the company's future cash flow.
Haitong International's main views are as follows:
1. Interest rates on US bonds are rising at a high level, why haven't they clearly suppressed the performance of innovative drugs?
Over the past few years, the Innovative Drug Index showed a clear negative correlation with the medium-term trend of US bond yields. Innovative pharmaceutical companies have the characteristics of long R&D cycles, late cash flow payment, and strong dependence on external financing. In particular, early biotech companies that have not yet been commercialized. Their value mainly comes from expectations of the forward cash flow of drugs under development, so they are more sensitive to changes in US bond interest rates and discount rates.
Since 2026, US bond yields have continued to rise, but both the Chinese and US innovative drug indices have risen, and the valuation pressure brought about by rising interest rates has not been a factor in the performance of the dominant sector. As of October 14, 2026, the Hong Kong Stock Connect Innovative Drug Index rose by about 10.1%, and the Nasdaq Biotech Index rose by about 20.4%; during the same period, the yield on US 10-year Treasury bonds rose from 4.15% to 5.19%, with a cumulative increase of about 104 bps. Judging from the 250 rolling correlation coefficient, the correlation coefficient between Hong Kong stock innovative drugs and US bond yields rose to +0.11, and the original negative correlation weakened markedly; the US NBI rose to +0.83.
II. Long-term revaluation of cash flow: the internal logic of strengthening innovative drugs under high interest rates
2.1 Valuation mechanism: How does long-term revaluation of cash flow affect the sensitivity of innovative drugs to US bond interest rates?
The sensitivity of innovative drug valuations to US bond yields mainly depends on the distribution of future cash flows and the level of discount rates. As the research pipeline enters the commercialization stage, authorized transactions, and R&D efficiency increases, some cash flow is expected to be realized earlier, or the probability of achieving it will be improved, thereby changing the expected distribution of future cash flow and easing the valuation pressure caused by rising interest rates.
When future cash flow expectations for innovative drugs improve substantially, the positive impact of industry fundamentals may exceed the valuation pressure of rising interest rates, becoming the main contradiction affecting the valuation of innovative drugs. As international cooperation such as technological progress, external product licensing, and joint development continues to advance, enterprises are expected to increase recent cash flow or push future cash flow forward in time, so that the value of enterprises that are originally highly dependent on long-term expectations can obtain more clear cash flow support, which in turn brings valuation flexibility that exceeds the negative impact of interest rate fluctuations. A simplified DCF model can intuitively demonstrate this valuation mechanism: the increase in valuation brought about by improved cash flow distribution may exceed the discount pressure caused by rising interest rates.
2.2 Innovative drugs are not generally on the rise: commercialization, BD, and AI all point to improved cash flow expectations
The US pharmaceutical market is clearly divided, and commercialization of new drugs, mergers and acquisitions, and AI pharmaceuticals have become important driving forces for the rise in the innovative drug sector. As of September 30, 2026, biotech ETFs (XBI) and pharmaceutical ETFs (IHE) have risen 29.42% and 19.51% respectively since the beginning of the year, significantly higher than the 10.07% of broad healthcare ETFs (XLV), while medical device ETFs (IHI) fell 17.35%. The strong performance of the innovative drug sector is mainly related to the reading of key clinical data on innovative technology therapies showing commercialization potential, the volume of commercialized varieties exceeding expectations, and the continuous acquisition of high-quality pipelines by multinational pharmaceutical companies (MNC). The penetration of AI technology into the pharmaceutical industry is becoming a long-term variable for improving the return rate of the pharmaceutical industry. It catalyzes the efficiency and cost reduction of traditional pharmaceutical methods in the short term, and there is long-term hope to explore new biological mechanism discoveries and solve more unmet disease needs.
The rise in the Hong Kong Stock Innovative Drug Index is also not a general increase; it is mainly driven by a few companies with clear cash flow payment paths. Of the 41 constituent stocks, only 16 companies rose and 25 fell; under the simulated scenario where the composition and positions remained the same, the total contribution of the top ten companies was about 19.98 percentage points, while the remaining 31 companies dragged down about 9.48 percentage points in total. Index revenue is mainly concentrated on three types of companies: first, innovative pharmaceutical companies that promote product commercialization and global BD to improve profits; second, R&D, production service companies with strong order growth and delivery capabilities; and third, tools and experimental service platforms that are expected to benefit from increased demand for AI R&D. Overall, the market places more emphasis on innovative drug companies' ability to deliver on actual value and future cash flow growth expectations.
3. Resonance between global supply and demand for innovative drugs drives continuous revaluation: patent cliffs release demand, and new technologies expand supply
3.1 Patent cliff pressure continues to show, and the demand for external procurement of innovative assets from large global pharmaceutical companies has been released. IQVIA anticipates that by 2030, more than $230 billion in biopharmaceutical sales will be at risk of losing its monopoly on the market. The demand for multinational pharmaceutical companies to supplement innovative assets through mergers and acquisitions, licensing and joint development continues to grow. The amount of global biomedical mergers and acquisitions in the first half of 2026 was about 130 billion US dollars, which is close to the scale of about 133 billion US dollars for the full year of 2025.
3.2 Supply side: New technologies and high-value innovative drugs continue to emerge, expanding the global innovative drug market space. The market space for innovative drugs not only depends on the demand for existing diseases, but is also driven by breakthroughs in new technology and improvements in treatment plans. Innovative technological therapies such as antibodies, ADCs, peptides, and micronucleic acid drugs continue to expand treatment boundaries in the fields of oncology, metabolism, self-immunity, cardiovascular diseases, etc., and promote the continuous emergence of new drugs with clinical competitiveness and commercialization potential. According to IQVIA data, the share of novel treatment technologies in global oncology clinical trial projects increased from about 11% in 2016 to about 33% in 2025; the average number of new active oncology ingredients (NAS) marketed globally increased from about 16 in 2015-2019 to about 26 in 2020-2024. The development of new technologies is driving the upgrading of innovative drug research and development structures and providing more choices for the world's largest pharmaceutical companies to obtain high-quality innovative assets.
3.3 China's innovative pharmaceuticals realized value by following the dual path of products going overseas and R&D assets going overseas, and more companies have crossed the break-even point. On the one hand, companies such as BeiGene and Legend Biotech share the global market space through overseas product launches and sales growth; on the other hand, Chinese Biotech companies have realized the international value of R&D assets through license-out. The continued increase in revenue brought about by products going overseas and the increase in authorized revenue brought about by overseas R&D assets have jointly pushed more Chinese innovative pharmaceutical companies to cross the break-even point, and profit improvements spread from a few leaders to more enterprises. Taking the top 20 companies in the Hang Seng Hong Kong Stock Connect Innovative Drug Index as a sample, the number of companies with positive net profit in 2023 and 2024 was 11 and 10, respectively. The number increased to 14 in 2025, and further reached 17 in the first half of 2026. The share of profitable companies increased from 50% in 2024 to 85% in the first half of 2026.
4. AI reshaping the paradigm for innovative drug development: opening up space for a new round of valuation and reevaluation
4.1 AI technology is expected to reverse the current state of anti-Moore's law in the pharmaceutical industry, change the R&D economy of innovative drugs, and expand growth boundaries
Innovative drug development has the characteristics of “long cycle, high investment, and high failure rate”, and R&D efficiency limits the return on investment in the industry for a long time. According to Deloitte data, the average R&D cost of a single new drug from discovery to marketing for the world's largest pharmaceutical companies rose from US$2.23 billion in 2024 to US$2.67 billion in 2025. Through computational prediction, molecular design, and experimental screening, AI technology is expected to improve the efficiency of the use of R&D resources, reduce capital and time investment in some R&D processes, and improve the problem of mismatch between long-term R&D investment and output in the industry.
The more profound value of AI is to expand the boundaries of new drug discovery and promote the transformation of drug development from project-by-project exploration to a large-scale, platform-based model. As AI models in the pharmaceutical and scientific research fields continue to advance, AI's ability to make scientific hypotheses increases, and 24-hour automated laboratories are widely used, disease mechanisms, potential targets, and molecular modes that were difficult to explore in the past are expected to become new R&D opportunities, making large-scale discovery of new drugs possible. This means that AI can not only improve the R&D economy of existing projects, but is also expected to expand the development pipeline and future commercialization opportunities for innovative drug companies, and further open up room for long-term growth in the industry.
4.2 AI spawns R&D “shovel sellers” and new business models, opening up room for growth in the innovative drug industry chain
Similar to computing power infrastructure, which is the first to benefit from the development of the AI industry, experimental verification and data platforms in innovative drug development, is expected to become the first link in the AI pharmaceutical industry chain to achieve commercial benefits. As AI drug development accelerates, demand for high-quality data and large-scale experimental verification is expected to continue to grow, and the industrial chain will generate new orders, revenue, and business models as a result.
First, high-quality data and testing platforms are expected to benefit from the growing demand for AI model training and scientific research exploration. Genetic sequencing, molecular testing, and clinical data platforms can expand original testing and data production capabilities to model training, data licensing, and R&D services, and Illumina and Tempus AI provide related business cases.
Second, in vitro experimental verification platforms are expected to be the first to benefit from the new experimental verification requirements brought about by the improved efficiency of AI design molecules. Businesses such as gene synthesis and protein expression benefit from increased demand for AI candidate molecule design and experimental verification. Kingsley's AI-enabled drug discovery related business revenue doubled year-on-year in the first half of 2026; Twist Bioscience accounted for about 38% of the annual order increase in FY2025, new orders contributed by AI drug discovery related customers, indicating that AI R&D demand has begun to be transformed into actual business opportunities.
Third, in vivo testing platforms are expected to benefit from the extension of drug verification requirements to subsequent R&D processes. After the candidate molecules are initially screened, animal models are still needed to verify their efficacy, safety, and mechanism of action in vivo. A platform with animal models, antibody development and proprietary data capabilities, which is expected to expand R&D cooperation and technical licensing services.
Fourth, the combination of AI and automated experiments is expected to spawn new platform-based businesses such as “molecular factories”, model training, and deployment. Related companies can not only provide single-test services, but may also continue to charge fees through integrated R&D platforms, special model training, and localized deployment, etc., to expand revenue sources from traditional R&D projects to technical services and R&D asset cooperation.
5. Innovative drugs are expected to become a highly flexible racetrack for AI applications, unleashing valuation flexibility that exceeds performance growth
5.1 Historically, a new round of revaluation of innovative drugs did not necessarily presuppose falling interest rates on US bonds. Industrial breakthroughs have repeatedly pushed pharmaceutical assets through interest rate pressure
The 2013 US Biotech market shows that an improvement in the innovation and R&D boom may drive a reassessment of expectations in the entire sector. From the end of April 2013 to the end of the year, the yield on US 10-year Treasury bonds rose from about 1.70% to 3.04%, up about 134 bps; during the same period, the NBI rose by about 65.6% for the whole year. New drug approvals, IPOs, and industrial transactions were active back then, driving the commercial value of innovative therapies to be re-evaluated.
The 2023 GLP-1 market shows that in a high interest rate environment, the market's rewards for commercial cash flow may be more concentrated. The yield on US 10-year Treasury bonds once reached 4.98%, and Eli Lilly's stock price continued to rise by about 59% throughout the year. The total returns of XBI and XLV during the same period were about 7.6% and 2.1%, respectively. Mounjaro's sales increased from US$483 million in 2022 to US$5.163 million in 2023, reflecting the importance of product commercialization to support the company's valuation.
5.2 Beyond cost reduction and efficiency: AI is expected to drive innovative drugs from performance improvement to revaluation of R&D assets
Innovative drugs are expected to become one of the directions with great growth potential and valuation flexibility in the AI application stage. The peculiarity is that the value brought by AI may far exceed current revenue and profit improvements. For most industries, AI mainly reflects value through efficiency improvements and profit growth in existing businesses; the value of innovative pharmaceutical companies depends on the potential commercialization value of the research pipeline, and their valuation depends not only on current profits, but also on factors such as expected future drug sales spikes, contribution to cash flow, and fulfillment time. Improving the efficiency of AI R&D is expected to improve the input-output ratio of existing projects, and may improve the cash flow conditions; large-scale drug discovery is expected to expand potential R&D assets and commercialization space, and drive forward long-term growth expectations. The latter need not shorten the long-term cash flow period, but it may increase the expected value of the company's future cash flow.
Therefore, a new round of revaluation of innovative drugs does not necessarily presuppose falling interest rates on US bonds. High interest rates will still put discount pressure on forward cash flow. Product marketing and global licensing cooperation have increased the visibility of recent revenue and cash flow, while AI is expected to further improve the economy and potential growth space of subsequent R&D pipelines. When positive revisions in industry fundamentals are sufficient to offset the negative impact of rising discount rates, innovative drugs may still be revalued in a high interest rate environment. Looking forward to the future, if US bond yields fall, innovative drugs are expected to have the dual resonance of AI-driven improvements in R&D economy and declining discount rates, driving forward growth expectations and valuation levels to rise simultaneously.
Risk warning: 1. AI technology's improvement in new drug development and clinical success rates falls short of expectations; 2. Clinical progress falls short of expectations; 3. New drug development failure, review and commercialization progress falls short of expectations; 4. Global license-out cooperation down payments and milestones are volatile, and one-time benefits are high; 5. The return on new orders, repayments, and capital expenses of AI R&D platforms falls short of expectations; 6. The competitive landscape of the industry has intensified; 7. Risk of changes in policies or review and approval systems related to AI and the pharmaceutical industry.