“AI Pharmaceuticals”: An Industry Created by New Imagination
2024-08-17
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Sun Xiaomeng, reporter of China Economic Weekly
As artificial intelligence technology becomes a powerful tool for creating new imaginations and future possibilities in thousands of industries, the biopharmaceutical industry is also being changed by AI.
Recently, the application of artificial intelligence in the pharmaceutical field has attracted widespread attention. "AI pharmaceutical" is believed to be likely to completely change the drug discovery and development process, and has triggered a wave of enthusiasm in the capital market. In the first half of 2024, the market recovered well, with 69 global AI pharmaceutical financings and an investment of US$3.336 billion; there were 22 AI pharmaceutical financings in China, with a financing amount of 1.809 billion yuan.
Currently, the global AI pharmaceutical industry has attracted technology giants such as Google, Microsoft and Amazon to join the game. At the same time, leading pharmaceutical companies such as Pfizer, Johnson & Johnson, AstraZeneca and Merck are actively deploying related research and development areas. So far, there are more than 100 AI pharmaceutical companies in China.
Favored by giants, with capital flowing in and entrepreneurship booming... What is the charm of AI pharmaceuticals? What are the opportunities? What are the challenges?
The past and present of AI pharmaceutical manufacturing
There is a very famous "double ten rule" in the traditional pharmaceutical field, that is, the R&D cost is $1 billion and the R&D cycle is 10 years. The latest data shows that the average R&D cost of innovative drugs worldwide is about $2.6 billion and the R&D cycle is 10.5 years. While pharmaceutical companies have high investments, they also face the high risk that new drugs may fail in the clinical trial stage.
New drug development is a complex and time-consuming process, which is generally divided into several major stages. The drug discovery stage includes the following steps: first, target confirmation, identifying disease-related biological molecules or pathways as potential drug targets; second, high-throughput screening, using automated technology to screen thousands to millions of compounds to find candidate drugs that can interact with target molecules; third, lead compound optimization, optimizing the initially screened compounds to improve their activity, selectivity and drug properties.
After drug discovery, there are preclinical research, clinical research, regulatory approval, and post-marketing monitoring of new drugs. AI can participate in the drug discovery stage, optimizing drug development through inductive reasoning and using computing power to accelerate the screening and optimization of lead compounds. AI can also play a role in the later stages of the process.
At present, AI tools have achieved some results in the drug discovery stage. For example, AlphaFold, a tool developed by Google's DeepMind, has significantly improved the efficiency of drug discovery by predicting the three-dimensional structure of proteins. It uses deep learning algorithms to bring breakthroughs in the field of molecular biology.
In addition, companies such as Insilico Medicine have also used AI technology to generate new drug molecules and successfully entered the clinical trial stage. Practice has shown that AI does have potential in drug screening and optimization, which can significantly shorten the drug discovery process and improve the screening success rate by training models.
Currently, many technology giants are bullish on the field of AI pharmaceuticals. These investments not only promote technological development, but also promote the application of AI technology in actual drug development. For example, Pfizer and IBM Watson Health are working together to explore the application of AI in cancer treatment.
There are three main types of companies in the AI pharmaceutical industry: technology giants, start-ups and large pharmaceutical companies. The company's business is divided according to the industry chain, mainly AI+biotech (using AI to independently develop innovative drugs), AI+CRO (using AI to deliver lead compounds and preclinical candidate compounds to customers), and AI+SaaS (only providing AI tools).
China's AI pharmaceutical industry has already laid out
In January 2022, the "14th Five-Year Plan for the Development of the Pharmaceutical Industry" jointly issued by the Ministry of Industry and Information Technology and nine other departments mentioned that it is necessary to explore the application of technologies such as artificial intelligence, cloud computing, and big data in the field of research and development, and to improve the efficiency of discovering new targets and new drugs through biological data mining and analysis and simulation calculations.
On July 30, the Shanghai Municipal Government issued the "Several Opinions of the General Office of the Shanghai Municipal People's Government on Supporting the Innovation and Development of the Whole Chain of the Biopharmaceutical Industry" (hereinafter referred to as the "Opinions"). It mentioned that it is necessary to support artificial intelligence technology to empower drug research and development, establish a mechanism for the open sharing of cohort research data, create high-quality corpora and industry data sets, and improve the cooperative utilization mechanism of medical and health insurance data resources.
The "Opinions" issued by Shanghai this time emphasized the need to give full play to the role of artificial intelligence technology in basic research, new drug development, medical services and other aspects.
As early as October 2021, Shanghai established the "Zhangjiang AI New Drug R&D Alliance". The alliance was founded and initiated by the Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai Advanced Research Institute of Zhejiang University, MediSci, Insilico Medicine, Shanghai Hansoh and other units, and was jointly established by 15 companies including Zhangjiang Group and Jingtai Technology.
On June 13 this year, Jingtai Technology was listed on the Hong Kong Stock Exchange. It is not only regarded as the "first Chinese AI pharmaceutical stock", but also the first hard technology company to be listed under the 18C rules. 16 of the 20 biotech companies with the highest revenue in the world in 2022 are its customers.
In the past few years, domestic AI pharmaceutical investment has experienced a roller coaster market. In the first half of this year, the industry gained momentum in the financing market. However, under the lively appearance, it is necessary to see that Chinese AI pharmaceutical companies are still in the early stages of development, and most pharmaceutical companies are still in the early rounds of the capital market. In addition, many investors have a wait-and-see attitude towards the field of AI pharmaceuticals.
Opportunities and challenges of AI pharmaceutical manufacturing
Although AI pharmaceuticals have shown great potential, they still face many challenges. The first is the problem of data quality and complexity. Drug development requires a large amount of high-quality data, and the current data quality bottleneck limits the further role of AI.
In addition, as the application of AI pharmaceuticals becomes more and more widespread, relevant regulations and ethical issues are becoming more and more important. In 2023, the U.S. Food and Drug Administration (FDA) issued guidelines on the application of AI in drug discovery, emphasizing the importance of risk control and regulatory standards.
At the same time, in terms of business model and industry ecology, although new technology companies have strong financial resources, large pharmaceutical companies are still strong in this field. Many start-ups have also performed well.
Although AI has performed well in some aspects, there are still obstacles to the transformation of technological achievements. So far, no new drugs developed entirely by AI have successfully entered the market. On the one hand, this is because AI technology itself is still in the development stage; on the other hand, as mentioned above, drug development is extremely complex. Even after the drug discovery stage is completed, there is still a lot of uncertainty in the subsequent steps.
It is foreseeable that with the continuous advancement of technology and continued capital investment, AI pharmaceutical manufacturing may achieve more breakthroughs in the future, but it will also face many challenges such as data management, business model adaptation, regulations and ethics, and technical limitations.
AI pharmaceutical manufacturing may be the “next future”, but the road ahead is long and arduous.
(This article was published in China Economic Weekly, Issue 15, 2024)
