AI pharmaceuticals are maturing, and data sharing needs to be broken

In early 2023, when ChatGPT (a chatbot program developed by OpenAI in the United States, released on November 30, 2022) swept the world, AI pharmaceuticals, that is, artificial intelligence-driven drug research and development, also stood on the new outlet.

According to AI consulting firm Deep Pharma Intelligence, as of December 2022, the total investment of 800 AI pharmaceutical companies worldwide reached 5.93 billion US dollars, a 27-fold increase in nine years. In the first quarter of this year, there were more than 28 investments in AI pharmaceutical companies, with an average investment of $38 million.

On the map of AI pharmaceuticals in China, Shanghai Zhangjiang, the "old pharmaceutical valley", occupies a leading position, leading the country from the number of enterprises to the scale of pipelines. According to the publicly disclosed information statistics of Zhangjiang Group, there are a total of 99 enterprises in the country involved in the field of AI+ medicine, of which 34 are in Shanghai, and 25 Zhangjiang enterprises account for 25% in the country; There are 83 and 30 projects of AI+ pharmaceutical products in the preclinical research stage and clinical trial stage, respectively, while Zhangjiang accounts for 47% and 40% in the country, respectively.

In October 2021, under the initiative of Chen Kaixian, Jiang Hualiang, Rao Zi and three academicians of the Chinese Academy of Sciences, Zhangjiang AI New Drug Alliance came into being. By 2025, Zhangjiang Pharmaceutical Valley's "AI smart drug ecology" is expected to gather 300 active institutions, 30 innovation consortiums and 30 enabling platforms, and AI is expected to help add 30 new Class A new drug pipelines (pipelines, referring to a number of drugs in the development stage of pharmaceutical companies, including preclinical and clinical research) every year.

Today, the power of AI to accelerate target and drug discovery has been recognized by all, and the real test is still in the clinical stage. After Exscientia stopped developing the world's first AI-designed drug to enter clinical trials, the latest bad news is that Benevolent AI, another British AI pharmaceutical leader, recently announced that a drug candidate for the treatment of atopic dermatitis failed to meet a secondary efficacy endpoint in a phase II clinical trial.

In the future, which AI-assisted design or even designed from scratch drugs can be the first to successfully cross the "valley of death" of Phase II clinical trials, only time can give the answer. But there is no denying that as more and more pharmaceutical companies open their arms to AI, AI-enabled drug design is already unstoppable.

AI pharmaceutical "head on"

The biopharmaceutical industry has long been known as the "Double Ten" rule, that from the start of research and development of a new drug to the final approval of the market takes an average of 10 years, the investment cost of about $1 billion - many industry reports estimate the figure is several times more. In addition to the long cycle times and high costs, pharmaceuticals are a high-risk business, with industry estimates putting the global success rate of new drug discovery at between 2% and 15%.

Why is it so hard to develop new drugs? On the one hand, the human proteome, the difficult drug targets account for more than 75%, conventional targets are about to be developed, the track is particularly crowded; On the other hand, a drug candidate must meet a combination of conditions in multiple dimensions: solubility, activity/selectivity, toxicity, metabolism, pharmacokinetics/efficacy, and composibility.

Today, 60 percent of the disease has no effective drug, and 50 to 70 percent of patients do not respond to blockbuster drugs. A large number of clinical needs are not being met, and the industry urgently needs new drug development tools and paradigms, so AI has attracted the attention of a large number of entrepreneurs and investors.

Before 2012, the application of AI technology in drug research and development was still in the early stage of exploration, mainly including target identification, drug molecular design, virtual screening and so on. In the following five years, with the increasing maturity of machine learning, deep learning and other technologies, the advantages of AI application in drug discovery are expanding, and the scope of application is also extended to clinical trial design and prediction, "old drugs and new" optimization design scenarios.

2017 is regarded as the starting point of AI pharmaceutical industrialization. In September of that year, American AI pharmaceutical startup Atomwise announced that it had received $45 million in financing, becoming the largest financing in the AI pharmaceutical field. Iconic AI pharmaceutical companies, such as Exscientia in the UK and BenevolentAI in the United States, also made important breakthroughs in this year, and small molecule drug candidates developed by AI (small molecule drugs mainly refer to chemical synthetic drugs) began to emerge.

Insilico Medicine, headquartered in Hong Kong, China, is the first company in the world to explore the use of generative adversarial network (GAN) and generative reinforcement learning (RL) artificial intelligence technology for drug discovery, and has become one of the leaders in the field of AI pharmaceutical, leading drug research and development center in Shanghai is located in Zhangjiang. In February 2021, InSI announced that for the first time in the world, artificial intelligence was used to discover INS001-055, a drug candidate with a new target and a new molecular structure, for the treatment of idiopathic pulmonary fibrosis.