The 6th GBAT 2024 Greater Bay Area Life Sciences Industry Summit will be released in the form of online special topics. From June 24, 2024, we will publish five special articles on life science, synthetic biology, biomanufacturing, biomedicine, and bioenergy around the theme of life sciences through the media matrix of Liuhe Business Research and Selection.
The dawn of a new era of life science has appeared, and the future will accelerate fission at an exponential speed, and various sci-fi scenes in the past will come into reality one by one, let us follow the wave of cutting-edge breakthroughs and industrial changes in life sciences, deduce the infinite possibilities of life sciences, and jointly open a new era full of infinite imagination.
In this issue, we bring the fourth report of the GBAT 2024 Greater Bay Area Life Science Industry Summit on biomedicine, analyzing how AI technology has revolutionized the field of drug research and development, bringing disruptive innovation and efficiency improvement to the pharmaceutical industry.
Body:
The full text is 4,614 words
Expected to read for 9 minutes
The R&D of traditional drugs is looking for a needle in a haystack, and AI solves the dilemma of traditional drug R&D
Traditional drug research and development is like looking for a needle in a haystack, with high investment, long cycle and low success rate, making pharmaceutical a high-risk gamble. The long journey from initial discovery to final marketing of a drug is accompanied by strict criteria and screening, and only about one of the tens of thousands of candidate compounds can pass all stages of testing and review and be approved by the FDA.
According to the data of UFTS Drug Development Research Center, in the process of traditional drug research and development, drug researchers often need to propose 5,000~10,000 compounds for drug screening based on experience, screen out about 250 compounds to enter preclinical research, find 5~10 compounds for clinical trials, and finally 1 compound is approved by the FDA through clinical testing.
Traditional drugs have high R&D investment, long cycle and low success rate
The drug R&D industry is facing the dilemma of the Double Ten Law and the Anti-Moore Law, and the traditional new drug R&D model is in urgent need of change. Traditional drug development usually uses a funnel screening model, with layers of screening in a massive compound library, and finally only a few compounds can be approved for marketing through clinical trials.
According to the statistics of Nature, a new drug needs to go through a long research and development process and layers of tests from the laboratory to the market, and requires a large amount of capital investment. The needle-in-a-haystack model is inefficient and has a declining success rate, making it increasingly difficult to meet patients' demand for new drugs.
The Double Ten Law: Pharmaceutical companies invest $1 billion in R&D and it takes 10 years to develop a new drug. In reality, a new drug, from R&D to approval for marketing, needs to go through a long and complex process from target discovery, lead compound screening, preclinical research, clinical trials, etc., which takes an average of 10~15 years, costs about 2.6 billion US dollars, and has a clinical success rate of less than 10%, and finally only a very few drug candidates can go to market through layers of screening.
Anti-Moore's Law: Pharmaceutical companies have been spending more on R&D over the past few decades, and the number of new drugs that can be exchanged for $1 billion is halved every nine years. Moore's Law states that when the price remains the same, the performance of semiconductor chips doubles every 18 months, and the performance will also double. The cost of new drug R&D is soaring, doubling every 9 years, mainly because the difficulty of new drug R&D is increasing, especially for complex diseases such as tumors and neurodegenerative diseases.
The low-hanging fruits in the field of drug research and development are gradually being picked up, further increasing the difficulty of new drug research and development. Over the past few decades, the pharmaceutical industry has discovered many revolutionary new drugs through empirical exploration, which can effectively improve the treatment of many common diseases. With the deepening of research, the molecular mechanism of many diseases is becoming more and more complex, and the research and development of new drugs has to dig into the root cause of the disease, which means that it is necessary to explore at a deeper level such as genes and proteins, and the technical difficulty and uncertainty have greatly increased.
The essence of AI pharma is a paradigm shift from relying on expert intuition to data-driven decision-making, from perceptual and intuitive to rational and data-based. In traditional drug development, expert intuition and experience play a key role, and researchers explore the development path based on their own knowledge and feelings, which is subjective and uncertain, especially in the field of complex diseases and new drug development where the disease mechanism is not yet clear. AI pharmaceuticals rely heavily on data and algorithms, continuously train iterative models, extract regular knowledge, and find a correct, efficient, and replicable drug development path.
AI affects and reshapes the pharmaceutical industry in an all-round way, and data in the AI pharmaceutical field is king
AI is influencing and reshaping the pharmaceutical industry in all aspects, fundamentally solving many problems in drug research and development, and promoting disruptive changes in the pharmaceutical industry. AI is leading the pharmaceutical industry into a new era of smarter, more efficient, and more personalized technology, accelerating drug target confirmation and lead discovery through rapid analysis of massive amounts of data. Algorithms are used to optimize drug molecule design, predict the critical properties of drug candidates, and significantly improve R&D efficiency and reduce costs. It helps to realize personalized and precise drug use, promote the transformation of drug research and development from mass to precision, improve the accessibility of new drugs, and make high-quality treatment more popular.
The biotech industry places a high value on AI technology and has high expectations, believing that cutting-edge technologies such as generative AI have the potential to bring about change in drug discovery, medical technology, biotechnology and other fields. According to EY's end-of-2023 survey, about 41% of biotech CEOs are looking to use generative AI to create concrete value for their companies.
AI will significantly improve the efficiency of drug development and greatly shorten the cycle of new drug development. Low R&D efficiency is a problem faced by pharmaceutical companies, and it takes more than ten years for new drugs to go from the laboratory to the market. AI can realize rapid analysis and mining of massive literature and data, accelerate drug target confirmation and lead compound discovery, use AI algorithms to optimize drug molecule design and synthesis pathways, shorten the lead compound optimization time, accurately predict key properties such as drug efficacy, toxicity, and pharmacokinetics of drug candidates, and improve the success rate of clinical trials.
Wang Mingtai, vice president of XtalPi, a Chinese AI pharmaceutical company, said that theoretically, the number of compounds that could be made into drugs is 10 to the 60th power, which is even greater than the number of atoms on earth. In traditional drug research and development, the screening of compounds that may become drugs mainly relies on manual experiments, which is inefficient, and the screening sequence is often based on the personal experience of the developer, with high uncertainty. Based on AI algorithms, it can complete the virtual screening of 1 billion compounds within 24 hours.
According to Tech Emergence, AI can save 40%~50% of the time in compound synthesis and screening, and will save the pharmaceutical industry $26 billion in compound screening costs every year.
AI will greatly reduce the cost of drug development, reduce the number of trial and error in the drug development process, and increase the success rate. AI technology can reduce the risk of drug obsolescence, improve the R&D hit rate, and greatly reduce the cost investment in the drug discovery stage through accurate data analysis and model construction, efficient screening of lead compounds, optimization of drug molecule design, and prediction of key properties of drug candidates. AI can optimize clinical trial design and accurately match patients, which can not only improve the success rate of trials, but also reduce trial costs.
According to BCG, a Boston consulting company, a clinical analysis of more than 100 AI native biotechnology companies, found that the success rate of AI-generated drug molecules in phase I clinical trials is as high as 80%~90%, with a historical average of about 50%; In phase II clinical trials, the success rate of AI discovery of drug molecules was 40%, which was comparable to the industry average. If it is assumed that the success rate of traditional drugs in phase III is comparable, the success rate of new drug research and development will increase from the current 5~10% to 9~18%, and the overall research and development efficiency of the pharmaceutical industry will be doubled.
AI promotes drug redirection and repurposing old drugs, which is of great significance for accelerating the development of new drugs and reducing R&D risks and costs. Compared with the research and development of new drugs, the new use of old drugs has accumulated a large amount of clinical data in terms of efficacy and safety, and the risk and cost of research and development are relatively low, and the approval is relatively easy. The research on the new use of traditional old drugs often relies on expert experience and literature research, which has problems such as inefficiency, randomness, and limitations.
AI greatly improves the efficiency of research on the new use of old drugs, efficiently realizes massive data mining, quickly integrates and analyzes massive biomedical data, including literature, patents, clinical trials, electronic medical records, etc., comprehensively excavates the association between drugs and diseases, discovers potential new drug indications, provides clues for the research of new uses of old drugs, reduces the cost of developing new drugs from scratch, and accelerates the process of drug launch.
AI will reshape the drug development process and model, and reshape the drug development industry pattern. Traditional drug R&D adopts a linear model, with each link proceeding sequentially, with slow progress and lack of flexibility. AI can not only accelerate each R&D process, but also break down barriers to achieve parallel R&D, real-time feedback, and dynamic optimization, making the drug R&D process more intelligent and efficient. Some pharmaceutical giants are working with AI companies to explore end-to-end AI solutions from early drug discovery to clinical development, and explore the creation of new intelligent R&D models and ecosystems.
AI will lead the era of personalized precision medicine and promote the transformation of the pharmaceutical industry from mass to precision. The research and development of new drugs is mainly for the mass market, and one-size-fits-all generic drugs dominate, and with the advent of the era of precision medicine, personalized medicine will become the general trend.
AI technology can deeply mine multi-omics data such as patient genes and proteins, as well as massive medical data such as electronic medical records, draw individual disease spectrums, identify key pathogenic genes and pathways, and develop precision drugs for specific patient groups and even individuals. AI can help precise drug administration and monitoring, reduce adverse reactions, and improve efficacy.
AI will drive the optimization of drug production and supply processes, improve drug accessibility, and reduce healthcare costs. AI can not only reduce the cost of new drug development, but also optimize the production process, improve supply chain management, and further reduce drug costs. AI-assisted diagnosis and treatment decision-making will improve medical efficiency, reduce over-treatment, and help reduce overall medical expenditure. AI will enable more innovative medicines to benefit the public, providing affordable, high-quality drug treatments.
Small innovation organizations will benefit from the huge potential of AI pharmaceuticals and become pioneers in pharmaceutical R&D and innovation. With the continuous improvement of the capital, technology and time threshold for innovative drug R&D, the field of new drug R&D has gradually become an arena for industrial giants with abundant funds. The rise of AI pharmaceutical technology provides new opportunities for small innovation organizations to bypass high R&D investment, focus on specific disease areas, accelerate new drug research and development, stand out in market competition, promote the diversification of innovation subjects in the pharmaceutical industry, break the monopoly pattern of industrial giants, and build a vibrant innovation ecosystem.
Morgan Stanley released a report in 2023 pointing out that through the use of AI technology, the application of AI in early-stage drug development in the next 10 years may be transformed into 50 new therapies, which will bring more than $50 billion in new sales; Drug manufacturers will be able to upload all test data to the cloud and use AI to identify more viable new drug combinations, which will give small academic start-ups the ability to outperform big pharma in the drug discovery race, with more than 20% of new drugs already coming from small innovative organizations, and the proportion will grow exponentially with the adoption of AI technology.
Data, algorithms, and computing power are the three core elements of AI technology, which profoundly affect the application of AI in the field of drug research and development.
Data, the food of AI models, and massive high-quality data are the prerequisites for efficient training of AI models, and biomedical big data provides rich nutrients for AI pharmaceutical models.
Algorithms, the core engine of AI models, deep learning, reinforcement learning and other new algorithms emerge one after another, greatly improving the perception, learning, and reasoning capabilities of AI systems, and injecting strong impetus into AI pharmaceuticals.
Computing power, the material basis for the application of AI models, provides the computing resources required for training and inference, and the rapid iteration of AI chips represented by GPUs greatly reduces the cost of computing power, providing effective support for AI pharmaceuticals.
Data resources in the pharmaceutical industry are more scarce and expensive, and AI data in the pharmaceutical field is king. Whether it is preclinical or clinical trial data, it condenses a lot of manpower and material resources of pharmaceutical companies, represents the core competitiveness, and high-quality private databases are the core moat of pharmaceutical companies. It is difficult to quickly catch up with the data advantage, and having a unique and complete pharmaceutical proprietary database is crucial to AI model training and is the cornerstone of building competitive barriers for AI pharmaceutical companies.
AI has become a catalyst for the innovation and development of the pharmaceutical industry, and the AI pharmaceutical industry has ushered in a period of rapid development
Driven by the new wave of AI set off by ChatGPT, major pharmaceutical companies and biotechnology companies around the world are actively developing AI pharmaceuticals. The AI pharmaceutical industry is increasingly favored by the capital market, and the industry penetration rate is increasing rapidly.
According to MedMarket Insights, the global AI pharmaceutical market size will be US$1.29 billion (+33%) in 2023 and is expected to maintain stable growth, reaching US$2.47 billion (+30.4%) in 2026, nearly doubling compared to 2023.
The size of the global AI pharmaceutical market
资料来源：MedMarket Insights、六合商业研选
China's AI pharmaceutical industry has ushered in a period of rapid development, and the market scale has expanded rapidly. The rapid development of biotechnology and AI technology, and the emergence of innovative AI models such as AlphaFold and ChatGPT, have driven the development of China's Al pharmaceutical market. According to the China Business Industry Research Institute, the size of China's AI pharmaceutical market in 2023 will be 1.02 billion yuan (+33.0%), and it is expected to maintain stable growth, reaching 2.49 billion yuan (+52.0%) in 2026, nearly 2.5 times the scale in 2023.
The size of China's AI pharmaceutical market
Source: China Business Industry Research Institute, Liuhe Commercial Research Selection
AI pharmaceutical, focusing on the three major fields of oncology, immunology, and neurology, to explore more effective treatment methods. AI is being fully applied in the field of pharmaceutical research and development, especially in the three challenging and promising directions of anti-tumor drugs, immunotherapy, and treatment of neurological diseases.
According to data from market analyst agency Deep Pharma Intelligence, in 2022, global AI pharmaceutical companies will focus on indications, including oncology, immunology, neurology, inflammation, cardiovascular, gastrointestinal, rare diseases and other diseases, accounting for 37%, 21%, 14%, 12%, 6%, 4%, 4%, and 2% respectively.
AI pharmaceutical companies have a wide range of pharmaceutical layouts, and the field of early-stage drug development is the most optimistic. According to data from market analysis agency Deep Pharma Intelligence, in 2022, the number of companies related to the global layout in the fields of early drug development, data processing, clinical development, end-to-end drug development, preclinical development, and drug reuse will be 392, 235, 149, 83, 392, and 57, accounting for 41.6%, 25.0%, 15.8%, 8.8%, 6.1%, and 2.8% respectively.
The capital market's enthusiasm for investment in the field of AI pharmaceuticals has cooled, and the total amount of financing in the field of AI+ drug research and development has declined. According to the data of the Intelligent Pharmaceutical Bureau, in 2023, there will be a total of 104 global AI+ drug R&D financing events, with a total financing amount of US$3.6 billion, a year-on-year decrease of more than 42%. As a comparison, in 2022, there will be a total of 144 global AI+ drug R&D-related financing events, with a total financing amount of US$6.2 billion (+47.6%); In 2021, there were a total of 73 global AI+ drug R&D-related financing events, with a total financing amount of US$4.2 billion.
Total investment in AI drug R&D
AI pharmaceutical investment and financing activities are mainly active in the United States and China, both exceeding the rest of the countries combined. According to the data of the Intelligent Pharmaceutical Administration, in 2023, there will be 48 AI drug R&D financing events in the United States, 32 in China, and 24 in other countries, accounting for 46.2%, 30.8%, and 23.1% respectively. In terms of financing funds, the United States, China, and other countries accounted for 80%, 10%, and 10% respectively, an increase of 3 percentage points, an increase of 1 percentage point, and a decrease of 4 percentage points compared with 2022.
The financing of AI pharmaceutical companies in China has seen an obvious generational disconnection, and the financing is still dominated by early-stage financing. In 2023, the leading companies XtalPi and Insilico Medicine went to Series D and applied for IPO listing on the Hong Kong Stock Exchange, and XtalPi passed the hearing of the Hong Kong Stock Exchange on May 26, 2024. The vast majority of other AI pharmaceutical companies that have received financing, except for Drug Ranch and Shenshi Technology, which have reached the C round, are in the angel round to the A+ round stage, and only one company has received the B+ round of financing.
Related Research Reports:
【Intelligent Era】Autonomous Driving: The moment of the iPhone car, the evolution of China's three major schools is taking shape
Battle of the Gods: American tech giants, from the Spring and Autumn Five Hegemons to the Warring States Seven Heroes|GBAT 2023 Greater Bay Area Intelligent Era Industry Summit
The second part of the trilogy of human future civilization: the special pre-sale of the intelligent era is open, the singularity is approaching, and the future has come
Zhao Yujie of Jiuyu Capital: Thinking in the era of intelligence, cognitive thinking, there are three waves of cognitive dividends of native, dimensionality reduction, and dimensionality upgrading
Zhao Yujie of Jiuyu Capital: Thinking in the era of intelligence, taking history as a mirror, the wave of science and technology, from the Internet to AI
Zhao Yujie of Jiuyu Capital: Thinking in the era of intelligence, cosmic perspective, from carbon-based organisms to silicon-based agents
One of the trilogy of human future civilization: the pre-sale of the metaverse topic is open, with 59 issues and 450,000 words
Zhao Yujie of Jiuyu Capital: 15,000 words of annual thinking collection of number one players, scientific and technological innovation, endless frontier
Zhao Yujie of Jiuyu Capital: 15,000 words of annual thinking set of smart electric vehicles, software definition, reshaping everything
Frontier Weekly: Embrace technology, insight into the future, 70 issues of the collection are packaged and delivered
The members of the Liuhe annual report were officially launched, and the research results of more than 5 years were systematically delivered
【Smart Electric Vehicle Special Pre-sale】The century-old automobile industry has accelerated its transformation, and the era of intelligent electric vehicles has opened
[Ready Player One Season 1 Pre-Sale]: Tech giants explore the future, and the number one player echelon rises
[Ready Player One Season 2 Pre-sale]: Technological innovation brings a paradigm shift and expands endless new frontiers
[First paid report + annual membership] 140,000-word in-depth report on live broadcast e-commerce: under the trillion-level GMV tuyere, giants are fighting and merging
The collection of science and technology sports reports was launched, and the deep integration of "technology + sports" has transformed sports in an all-round way
【Blockbuster】365 star companies, nearly 600 reports, and a panoramic presentation of Liuhejun's research results for more than 4 years
Zhao Yujie of Jiuyu Capital: CES Experience, open your brain, super technology giants will take over everything
Zhao Yujie of Jiuyu Capital: 5G opens a new cycle and enters the era of exploration in the online world|GBAT 2019 Greater Bay Area 5G Summit
Zhao Yujie of Jiuyu Capital: Seize the huge trend dividend of e-cigarettes, and seize the changes and changes in industrial transformation
[IPO Observation] Quarter 1: A collection of 11 in-depth research reports from SMIC, Cambrian, SMOORE, Pop Mart, Anker Innovations, etc
[IPO Observation] Season 2: A collection of in-depth research reports from 12 companies, including Ideal, Xpeng, Shell, Ant, Snowflake, and Palantir
[IPO Observation] Season 3: A collection of in-depth research reports from 12 companies including Coinbase, Roblox, Kuaishou, and Wuxin Technology
Annual Observation 2019 Series Collection: It lasted more than 3 months, with more than 200,000 words and nearly 500 pages, reviewing the past, looking forward to the future, and gaining insight into change and change
【Collector's Edition】Liuhe Treasure Book: A panoramic scan of 300 star companies, which lasted 3 years, 2.1 million words and more than 5,000 pages, is highly recommended
Zhao Yujie of Jiuyu Capital: Fragmented thinking on the smart electric vehicle industry
Zhao Yujie of Jiuyu Capital: Jiugong Grid Analysis Method, Tao and Art in the Field of Language and Mathematics Education and Training
[2023 Returning to the Hometown] The post-90s and post-00s friends have a 10,000-word record, life is back on track, and the Spring Festival is stronger
[2022 Hometown Experience] 20 post-90s and post-00s with 20,000 words, 4 countries and 13 places, comprehensively showing the differences in epidemic prevention at home and abroad, Spring Festival atmosphere, and development status
[2021 Homecoming Experience] 22 post-90s 20,000 words, the collision of the Chinese New Year on the spot and the return to their hometowns for the New Year, showing a real, three-dimensional and transformative China
[2020 Homecoming Experience] 20 post-90s 20,000 words, special Spring Festival, the collective memory of the times
【Blockbuster】22 "post-90s" 20,000 words of experience and experience of returning to their hometowns, telling about their eyes in China's counties, towns, and rural areas
Liuhejun's 3rd birthday, TOP 60 classic research reports are recommended
Afternoon tea, the Three Kingdoms Killing in the Internet world
5G boosts AR to open a new industrial cycle, and AR glasses open the era of dedicated AR terminals
The new business infrastructure continues to be enriched and improved, enabling the rise of new brands, new models and new products, and creating a new and diversified lifestyle
【Blockbuster】China's new economy leader, a compilation of 20 reports on listing in Hong Kong and the United States
Knowledge service + payment + audio, opening up new industry-level opportunities for content production, the knowledge economy can be expected to be 100 billion yuan in 10 years
From the 4-year replacement of the best-selling list of the App Store, watch the rise of content payment
Thinking about the new third board breaking 10,000 yuan: the daily trading volume of the new third board is 100 times in 10 years?
Zhao Yujie of Jiuyu Capital: Technology Changes Consumption and Makes Life Better|2017 GNEC New Economy and New Consumption Summit
Zhao Yujie of Jiuyu Capital: The Law of Time in the Entrepreneurial Era, Parallel Time for Developing Users|2016 GNEC New Economy and New Intelligence Summit
Zhao Yujie of Jiuyu Capital: The Internet Leads the New Economy, Content Entrepreneurship Connects the New Ecology|2016 GNEC New Economy and New Marketing Summit
Be sure to read the disclaimer and risk warning