Artificial intelligence pharmaceuticals help the innovation and development of Chinese pharmaceutical industry

Artificial intelligence (AI) technology is reshaping the pharmaceutical industry in terms of cost and efficiency.China's AI pharmaceuticals got off to a slightly later start than those in Europe and the United States, but have grown rapidly with more data, algorithms and other advantages.According to relevant experts, AI pharmaceuticals will be an opportunity to overtake the domestic pharmaceutical industry at a bend in the curve.It is necessary to use AI pharmaceuticals as an entry point to strengthen forward-looking policy support for this emerging field, promote original and independent innovation in China's entire innovative pharmaceutical industry, and ultimately realize the export of Chinese innovation.

Advantages of late-stage development' for China's AI pharmaceutical industry Adjust font size:In recent years, local AI pharmaceutical companies have emerged in China, covering the whole chain of new drug development, covering multiple stages including target identification and certification, drug discovery, preclinical research and clinical research.Experts say the United States and Europe are in the early stages of AI pharma 3.0, while China is in the early stages of AI pharma 2.0.Most domestic AI pharmaceutical companies are in the phase of animal testing, efficacy and toxicology validation.Later this year, they are likely to enter the preclinical stage of candidate compounds and are expected to be in the early stages of Phase 3.0 within two to three years.

The United States still dominates the global AI drug pipeline layout.According to the statistics of the think tank "Smart Medicine Bureau", as of June 20, there were 26 AI pharmaceutical companies and about 51 AI-assisted drug pipelines entering clinical phase I. Among them, more than 80 percent are American enterprises, and only three Chinese enterprises are Insilicon Intelligence, Unknown Jun and Iceland.Most of the listed leading AI pharmaceutical companies are European and US companies, with no Chinese companies.

Dr Wang Lin, head of the Asia-Pacific development center of Japanese pharmaceutical company Takeda, said in an interview that China's local AI companies and biotech companies are rapidly improving their capabilities in AI-assisted drug research and development.Some local companies have grown out of proprietary development platforms and even started to explore frontier fields where no global company has ever set foot, such as small molecule crystal structure prediction and primary drug design.

Large amounts of capital are starting to flow into AI new drug research and development companies in China starting from 2021.Within a month of that year, three Chinese AI pharmaceutical companies received seed round funding.In the past two years, there have been three financing projects that have attracted considerable attention in the industry.The first is Hong Kong-based Intech, which raised $255 million last year to advance AI drug candidates into clinical trials and algorithms tweaked to find morenew targets.Beijing Wangshi Wisdom Technology Co., Ltd. also successfully raised $100 million in April the same year.In September 2020, Shenzhen-based Jingtai Technology also successfully raised $319 million.In addition, Chinese internet giants such as Tencent, Baidu and ByteDance have also turned their formidable AI computing power into drug development and design.

"China has a unique advantage in using AI technology to assist the research and development of new drugs, which will bring a historical opportunity for the domestic pharmaceutical industry to overtake in the curve.If they can flexibly apply this emerging technology, domestic pharmaceutical companies may become industry leaders on the global scale and enter the leading ranks.""said King Lin.

On the one hand, sufficient big data is crucial for AI training.China has a large population base and a sizeable hospital size, which is more conducive to large-scale data collection and integration.Second, there are about 3,000 CRO companies in China, which makes it possible for pharmaceutical companies to incorporate multiple CRO companies into drug development and conduct multiple trials in parallel: comparing different results is necessary for AI learning to progress, reduce costs and improve quality.

However, experts say China is more competitive in the AI sector and less so in pharmaceuticals..Main intelligent drug design platform of biotech companies round one wisdom, founder and CEO Dr Pan Lurong told reporters that Europe and the United States and our country in the aspect of AI algorithm completely without gap, even worse, but the understanding of data and application of biology and translational medicine infrastructure, knowledge, talent reserves, As well as the standard and quality management, industrial chain and supply chain of the entire pharmaceutical industry and foreign countries gap.Duan Hongliang, director of the Intelligent Pharmaceutical Research Institute at Zhejiang University of Technology, agreed that China's AI level is comparable to that of the United States, but the pharmaceutical industry lags far behind.Integration with the pharmaceutical industry is more difficult in the context of integrating AI with various industries and will not happen overnight.There is a need to respect the laws of drug research and development and take the time to polish them.

The challenges and risks of "blending the old with the new"Although AI has permeated every link of pharmaceuticalresearch and development, the combination of an emerging industry and a traditional industry still faces many challenges and risks, such as data, computing power and policy, according to the report.According to relevant experts, the AI pharmaceutical industry has the following challenges and risks, which are the key points that China needs to focus on to develop the industry.

Data and power issues. Ren Feng, an industry expert, believes that in the future, competition in AI pharmaceuticals will shift from algorithmic competition to data competition. The primary challenge is the amount of data, and it is only with the continuous input of huge amounts of clean data that AI models can be adequately trained and their accuracy improve. Second, there is the issue of data normalization. Currently, most data comes from public data, such as scientific research grants and publications. Data cleaning and integration is more time-consuming and laborious than AI modeling. Duan Hongliang, director of the Institute of Intelligent Pharmaceutical Research at Zhejiang University of Technology, said most Chinese enterprises currently have low quantity and quality of drug research and development data obtained through public databases, and need to generate and accumulate data from chemical and biological laboratories. 

Uncertainty in the discovery of new drugs."The biggest risk and challenge in the development of innovative drugs is that our understanding of disease is still shallow," claimed Pan."Even with advances in our understanding of the biology and pathology of various disease subsectors over the past 20 years, aided by molecular biology and human genomics, much remains unknown."Moreover, from an overall operational point of view, due to external influences such as funding and policy environment, the time span for the development of improved drugs is prolonged and therefore many useful scientific projects cannot be carried out."If the scientist who proposed the project is not persistent sufficiently in the face of doubts in the process, in the face of the funding, the industry environment and other aspects of the resistance to move forward, even the right idea may give up halfway."Therefore, policies and industrial capital are crucial to support innovative teams and scientists, Pan suggested.

Domain integration "acclimatization".AI Pharma is a collision between a largely closed and secretive industry and one of the most open.According to Pan, the combination of AI and pharmaceuticals is a process of reintegrating the knowledge systems and methodologies of biological experiments and computer science.The two disciplines are diametrically opposed: giant international pharmaceutical companies have been around for hundreds of years, with a wealth of knowledge, experience and data, but strict barriers.To this day, the pharmaceutical industry, built on expert experience, is naturally resistant to embracing digitalization.The field of AI, however, emphasizes "openness" and the breadth and quality of training data is critical.Guo Tiannan, doctoral supervisor of College of LifeSciences, Westlake University and founder of Xihu Omi (Hangzhou) Biotechnology Co., LTD., also believes that pharmaceutical is a conservative field.At present, it is difficult for big pharmaceutical companies to change the framework, and it is veryexpensive for traditional pharmaceutical companies to innovate.Instead, new companies will emerge and the industry will face a shake-up.

There is an extreme shortage of compound talent.Experts interviewed all pointed to the lack of sophisticated talent as the industry's biggest pain point, with China's shortage of such talent particularly acute.Ren said there are still a few people who understand traditional drug research and development and believe in AI, or are willing to use AI technology for innovative drug research and development. AI Pharma needs more people with traditional experience who can embrace AI technology with open minds. Pan also believes that there are too few talents with a combined background in biology, chemistry, medicine and AI technology, and that expert teams also face communication problems in different fields. In addition, there is a lack of AI talent for top-level design, which requires not only a background in algorithmic engineering, but also interdisciplinary training in AI systems engineering and biochemistry to implement top-level architectures and land technologies.

China's talent training system in this field needs to be improved, Guo said.Biomedical scientists are scientists, and the path of development is undergraduate, graduate, direct PhD, abroad; if a bachelor who majored in computer science finds a high-paying job straight away, his income will drop significantly if he works in AI and goes to a lifesciences-related institution. Most people who know business are in traditional businesses. It is easy to find business partners abroad, but China is relatively short of them, and university teachers or scientific research workers face institutional resistance to entrepreneurship.

The international political environment affects cooperation.Currently, uncertainties in the international environment, such as pandemics and political factors, have had a negative impact on scientific research exchanges and international cooperation, such as supply chains, talent flows and conference hosting, hindering the research and development of innovative AI drugs. According to Pan, any innovative drug research and development is now inseparable from the global industrial chain, and outsourcing research and development services has become very mature. For example, CRO services, from early stage chemical and biological synthesis to in vitro and clinical trials, are undertaken by a large number of global segmented companies, and China also undertakes a significant part of the industrial chain. Therefore, it is impossible to promote truly innovative drug research projects solely on the strength of one country and ultimately as a result of international cooperation.

China urgently activates AI pharmaceutical sector WAM BEIJING, Oct.Relevant experts suggested that the vitality of China's AI pharmaceutical industry should be comprehensively stimulated from the system, and support should be given from the perspectives of talent training, regulatory approval, park construction and data management, so as to promote AI pharmaceutical to realize the "revolution" of innovative drug research and development in China.One is to strengthen cross-disciplinary personnel training and attract transnational talent.According to relevant experts, 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.

We will speed up the training of interdisciplinary talents.Duan said barriers to professional talent in computing and biomedicine need to be broken down, with a focus on training compound talent. Guo suggested that biological scientists who specialize in one field and have narrow horizons have little incentive to move to another industry to learn something new. A mechanism could be set up to encourage some biomedical doctors to start their own businesses.Zhejiang University, for example, can recruit only one lifesciences PhD supervisor in three years on average, which does not give full play to the teaching capabilities of a large number of top universities. More institutional support is needed for scientific researchers, and a large number of senior talents are needed for transformation projects. In addition to seeking authoritative experts in the field for resource allocation and project review, investors are an assessment group that is relatively more objective and perceptive.

Full mobilization of multinational talent. At present, overseas talent in AI pharmaceuticals is more developed than Chinese talent, Ren said, adding that he hopes there will be more favorable policies to facilitate the introduction of high-level overseas talent. Pan also believes in flexible working hours, diverse incentives and strong mobilization of global resources through online and offline collaboration. At present, many of the core R&D personnel of foreign front-line pharmaceutical companies are Chinese, so we should especially strive for this group. In terms of policy, visa policies can be relaxed to attract workers with special skills and ensure a better living and research environment for them.

The second is to speed up regulatory approvals on a prospective basis.In order to meet the urgent clinical needs or under special conditions, some foreign regulatory agencies try to reduce part of the preclinical research on the basis of full support of AI big data to accelerate the development process of new drugs, or even directly accelerate to the stage of human clinical trials. Wang said he hoped that China's Food and Drug Administration and other regulatory authorities would continue to scientifically evaluate the latest regulatory measures of foreign regulators on the basis of accelerating the introduction of innovative drugs with clinical value, and formulate more forward-looking policies and regulations based on the actual situation and needs of China. For example, in certain areas, if suitable AI techniques are available to build virtual animal models for testing, it can also be recognized as a reference for preclinical study effects..Ren Feng also said that he expects the regulatory authorities to shorten the waiting time for the application and approval of clinical trials of AI new drugs, and AI pharmaceutical companies also expect to cooperate with the regulatory authorities to formulate and improve industry standards, so as to make the development of AI pharmaceuticals in China more standardized.

Third, we will promote inter-disciplinary industrial parks. Ren said AI pharmaceutical is an interdisciplinary discipline, and the government is expected to jointly build AI, biopharmaceutical and other interdisciplinary incubation parks, upstream and downstream of the industry, to form a good industrial ecosystem. Some support facilities could be built in the park, such as a supercomputing center to provide computing power support and a shared laboratory to validate early-stage AI drug research and development.

"Data security and privacy should be a top consideration when evaluating whether to adopt new AI algorithms or digital tools. "Pan also believes that there is a conflict between the confidentiality of data in the pharmaceutical sector and the reliance on data in the AI sector, which needs to be addressed through new encryption technologies, industry cooperation mechanisms, and innovative commercial management mechanisms for data assets.