Relevant experts believe that AI pharmaceuticals will become an opportunity for the domestic pharmaceutical industry to overtake the curve. AI pharmaceuticals should be used as the entry point to strengthen forward-looking policy support for this emerging field and promote original and independent innovation in the entire Chinese innovative pharmaceutical industry. Ultimately, Achieve the export of Chinese innovation overseas.
In recent years, China's local AI pharmaceutical companies have continued to emerge, involving the entire chain of new drug research and development, covering Multiple stages of target identification and qualification, drug discovery, preclinical research and clinical research. Relevant experts believe that European and American countries are currently in the early stage of AI pharmaceutical 3.0, and China is in the early stage of 2.0. Most of the domestic AI pharmaceutical companies are in the animal testing, efficacy and toxicology verification stages. They may enter the pre-clinical candidate compound stage later this year and are expected to enter the 3.0 early stage in two to three years.
The United States still dominates the global AI drug pipeline layout. According to statistics from the think tank "Smart Drug Bureau", as of June 20, there are 26 AI pharmaceutical companies in the world and about 51 AI-assisted clinical trials. drug pipeline. Among them, more than 80% are American companies, and there are only three Chinese companies, Insilicon Intelligent, Unknown Jun, and Bingzhou Stone. The leading AI pharmaceutical companies that have been listed are basically European and American companies, and there are no Chinese companies yet.
Dr. Wang Lin, head of the Japanese pharmaceutical company Takeda Asia Pacific Development Center, said in an interview with reporters that China’s local AI companies and biotechnology companies have rapidly improved their capabilities in AI-assisted drug research and development. Some local companies have developed patented development platforms and have even begun to explore cutting-edge areas that have not yet been explored by companies around the world, such as small molecule crystal structure prediction and primary drug design.
Starting in 2021, a large amount of domestic funds began to enter AI new drug research and development companies. Within one month of that year, 3 Chinese AI pharmaceutical companies received seed round financing. In the past two years, there have been three financing projects that have attracted much attention in the industry. First, Insilicon, headquartered in Hong Kong, successfully raised US$255 million last year to advance AI research and development of drug candidates into clinical trials and to advance algorithm adjustments to discover more new targets. Beijing Wangshi Smart Technology Co., Ltd. also successfully raised US$100 million in April of the same year. In September 2020, Shenzhen-based Jingtai Technology also successfully raised US$319 million. In addition, domestic Internet giants such as Tencent, Baidu, and ByteDance have also turned their strong AI computing power to the field of drug development and design.
China has unique advantages in using AI technology to assist in the research and development of new drugs, which will bring historical opportunities for overtaking in corners for the domestic pharmaceutical industry. If this emerging technology can be flexibly applied, domestic pharmaceutical companies may become industry leaders and enter the leading ranks globally. "Wang Lin said.
On the one hand, sufficient big data is the key to training AI. The domestic population base is huge and the hospital scale is considerable, which is more conducive to collecting and integrating large-scale data. Secondly, China currently has about 3,000 CROs (i.e. Contract Outsourcing Research Organization) company has created the possibility for pharmaceutical companies to include multiple CRO companies in drug development to conduct multiple trials in parallel: comparing different results is a necessary process for AI learning and progress, and can also reduce costs and improve Quality.
However, relevant experts believe that my country is more competitive in the AI part and slightly inferior in the pharmaceutical part. Dr. Pan Lurong, founder and CEO of Yuanyi Smart, which specializes in intelligent drug design platforms. Speaking to reporters, my country has no gap with Europe and the United States at the AI algorithm level, or even worse, but the understanding and application of data, the infrastructure of biology and translational medicine, the improvement of the knowledge system, the talent pool, and the entire The standards and quality management, industrial chain and supply chain of the pharmaceutical industry are far behind those of foreign countries. Duan Hongliang, director of the Intelligent Pharmaceutical Research Institute of Zhejiang University of Technology, also believes that China’s AI level is comparable to that of the United States, but the pharmaceutical industry lags far behind. Among the integrations with various industries, integration with the pharmaceutical industry is more difficult and will not be achieved overnight. We should respect the rules of drug research and development and spend time polishing it.
Although artificial intelligence Intelligence has penetrated into every aspect of pharmaceutical research and development, but the combination of an emerging industry and a traditional industry still faces many challenges and risks such as data, computing power, and policies. Relevant experts believe that the AI pharmaceutical industry has the following challenges and risks, which are also the development of our country. The key points that the industry needs to focus on are data and computing power.
Industry expert Ren Feng believes that the primary challenge in the future of AI pharmaceutical competition will be from algorithm competition to data competition. , only the continuous input of massive clean data can fully train the AI model and improve its accuracy. Secondly, there is the issue of data standardization. Currently, most data comes from public data such as scientific research funds and publications, and data cleaning and integration is more time-consuming and laborious than AI modeling. Duan Hongliang, dean of the Intelligent Pharmaceutical Research Institute of Zhejiang University of Technology, said that most of the drug research and development data obtained by most companies in my country through public databases are of low quality and need to generate and accumulate data from chemical and biological laboratories. In addition, there are limitations in computing power. Simulating the spatial conformation of a protein or molecule requires high accuracy, and currently even supercomputers cannot exhaust all combinations.
The uncertainty of new drug research and development. Pan Lurong said that the biggest risk and challenge in innovative drug research and development is that human beings’ understanding of diseases is still superficial. In the past 20 years, even though our understanding of biology and pathology in various disease subdivisions has gradually improved, some With the help of molecular biology and human genomics, there are still a lot of unknowns. In addition, from the overall operation point of view, the time span of new drug research and development is long, so many good scientific projects cannot continue to be carried out due to various external influences such as funding and policy environment. "If the scientists who initiated the project are not persistent enough to face the various doubts in the process and continue to move forward in the face of various obstacles such as funding and the industrial environment, even the right idea may be abandoned halfway." Pan Lurong said, therefore the policy and industrial capital are important to support innovative teams and scientists.
Field integration is "acclimated to the local environment". AI pharmaceuticals is a collision between a highly closed and confidential industry and the most open industry. Pan Lurong said that the combination of AI and pharmaceuticals is a process of reintegrating the knowledge systems and methodologies of biological experimental subjects and computer subjects. The temperaments of the two are completely opposite: large international pharmaceutical companies have been developing for hundreds of years, and they have accumulated rich knowledge, experience and data but have strict barriers. . To this day, the pharmaceutical industry is still based on expert experience and has a natural resistance to embracing digitalization. The AI field emphasizes "openness", and the breadth and quality of training data are very important. Guo Tiannan, doctoral supervisor at the School of Life Sciences of Westlake University and founder of Westlake Omi (Hangzhou) Biotechnology Co., Ltd., also believes that pharmaceuticals are a conservative field. It is currently difficult for giant pharmaceutical companies to change their frameworks. The cost of innovation for traditional pharmaceutical companies is very high, but newly created Companies will emerge and the industry will be reshuffled.
There is an extreme lack of comprehensive talents. The interviewed experts all pointed out that the lack of comprehensive talents is the biggest pain point of the industry, and the shortage of such talents is particularly serious in my country. Ren Feng said that there are still only a few people who understand traditional drug research and development, but also believe in AI or are willing to use AI technology to develop innovative drugs. AI pharmaceuticals need more people with traditional experience and the ability to accept AI technology with an open mind. Pan Lurong also believes that there are too few talents with combined backgrounds in biology, chemistry, medicine and AI technology, and expert teams also face communication and integration problems in different fields. In addition, my country is short of AI talents for top-level design. Such talents must not only have a background in algorithm engineering, but also need cross-disciplinary training in AI systems engineering and biochemistry in order to realize the top-level architecture and implement the technology.
Guo Tiannan said that my country’s talent training system in this field needs to be improved. Biomedicine are all scientists, and the development path is undergraduate, postgraduate study, direct Ph.D., and going abroad; undergraduates majoring in computer science can directly find high-paying jobs, and those who do AI will have a much lower income if they enter life science-related institutions; while most people who understand business are in traditional enterprise. It is easy to find business partners abroad, but there are relatively few in China. University teachers or scientific researchers face institutional resistance when starting their own businesses.
The international political environment affects cooperation. At present, the uncertainty of the international environment such as the epidemic and political factors has a negative impact on scientific research exchanges and international cooperation such as supply chains, talent flows, and conferences, and hinders the research and development of AI innovative drugs. Pan Lurong said that any innovative drug research and development is now inseparable from the global industrial chain, and outsourcing R&D services are very mature. For example, CRO services, from early chemistry and biosynthesis to in vitro testing and clinical trials, are undertaken by many segmented companies around the world, and domestic companies also undertake a significant part of the industry chain. Therefore, to promote a truly innovative drug research project, it is impossible to rely entirely on the strength of one country. It will ultimately be the result of international cooperation.
Relevant experts suggest that the vitality of my country’s AI pharmaceutical industry should be fully stimulated from the institutional perspective, and support should be provided from multiple perspectives such as talent training, regulatory approval, park construction, and data management. Promote AI pharmaceuticals to realize the "revolution" of innovative drug research and development in my country.
First, strengthen the cultivation of cross-cutting talents and attract transnational talents. Relevant experts believe that AI pharmaceuticals is a very cutting-edge field, and there is a large talent gap between China and foreign countries. Measures should be taken to fully mobilize global talent resources.
Accelerate the cultivation of cross-cutting talents. Duan Hongliang said that it is necessary to break down the barriers to computer and biomedical professionals and focus on cultivating compound talents. Guo Tiannan suggested that biological scientists have specialized fields and narrow horizons, and it is difficult to have the motivation to jump to another industry to learn new things. A mechanism can be set up to encourage some biomedical Ph.D.s to start their own businesses. In addition, there are too few Ph.D. places in the field of life sciences in universities. For example, Zhejiang University can only recruit one student for a doctoral degree in life sciences every three years. It is impossible to bring into play the capabilities of a large number of top university professors. More support needs to be given to scientific researchers in the system, and a group of senior talents can do this. Conversion project. In resource allocation and project review, in addition to seeking authoritative experts in the field, investors are also an evaluation group, which is relatively more objective and sensitive.
Fully mobilize transnational talents. Ren Feng said that at present, overseas talents in the field of AI pharmaceuticals are more developed than domestic ones, and he hopes that more preferential policies will be introduced to facilitate the introduction of high-level overseas talents. Pan Lurong also believes that it is necessary to have flexible working hours, diverse incentives, and use online and offline collaboration models to effectively mobilize global resources. At present, the core R&D personnel of many first-line foreign pharmaceutical companies are Chinese, and this group should be especially strived for. In terms of policy, relevant visa policies can be relaxed to attract workers with special skills and ensure a better living and scientific research environment for them.
Second, proactively accelerate regulatory approval. In order to meet urgent clinical needs or under special conditions, some foreign regulatory agencies have tried to reduce or exempt some pre-clinical research on the basis of sufficient AI big data support to speed up the development of new drugs, and even directly accelerate to the human clinical trial stage. Wang Lin said that he hopes that my country’s Food and Drug Administration and other regulatory authorities will continue to scientifically evaluate the latest regulatory measures of foreign regulatory agencies on the basis of accelerating the introduction of innovative drugs with clinical value, and formulate more forward-looking policies and regulations based on domestic actual conditions and needs. For example, in some specific fields, if suitable AI technology is available, virtual animal models can be established for testing, and they can also be recognized as a reference for the effectiveness of preclinical research. Ren Feng also said that he expects regulatory authorities to shorten the waiting time for approval of clinical trial applications for new AI drugs. AI pharmaceutical companies also hope to cooperate with regulatory authorities to formulate and improve industry standards so that AI pharmaceuticals can develop more standardized domestically.
Third, promote the construction of interdisciplinary industrial parks. Ren Feng said that AI pharmaceuticals are interdisciplinary, and he hopes that the government-led construction of interdisciplinary incubation parks for artificial intelligence and biopharmaceuticals will unite the upstream and downstream industries to form a good industrial ecosystem. The park can build some supporting facilities, such as a supercomputing center to provide computing power support, a shared laboratory that can verify early AI drug research and development, etc.
Fourth, strengthen data and privacy management. Wang Lin said that AI pharmaceuticals involve a large amount of data support and application. When relevant companies evaluate whether to adopt emerging AI algorithms or digital tools, the primary consideration should be data security and privacy protection. Pan Lurong also believes that there is a contradiction between the confidentiality of data in the pharmaceutical field and the dependence on data in the AI field, which requires new encryption technology, industry cooperation mechanisms, and innovative data asset commercial management mechanisms to resolve.
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