Recently, AI drug discovery company Exscientia announced that it has raised US$60 million in Series C financing. This round of financing was led by Novo Holdings, followed by Evotec, BMS and GT Healthcare Capital. The aim is to advance the drug development pipeline and support AI biology as part of its “full stack capabilities”. Its "full stack capability" refers to the full cycle from the initial stage of drug development to the market.
It is reported that Exscientia was established in 2012 as a high-tech company that uses artificial intelligence to design molecular compounds to accelerate drug discovery. The AI platform Centaur Chemist it develops is dedicated to the automation of drug research and development, and is a new method to improve productivity and drug efficacy. Currently, Exscientia has developed the first drugs created using artificial intelligence, which will be clinically tested on humans. This drug is used to treat obsessive-compulsive disorder. It takes less than a year from the conceptual design to the production of capsules for clinical testing. Earlier, it was reported that human trials began in March this year.
In fact, in recent years, a series of complex links such as drug target determination, lead compound screening, and clinical trial demonstration have made the development of new drugs a "long march." "And the "big trouble" in the field of medicine. According to a research report released by Deloitte, in 2017, the top 12 biopharmaceutical giants in the world only had a 3.2% return on investment in research and development, at a low level for 8 years. Even worse, the cost of successfully launching a new drug has increased from US$1.188 billion in 2010 to US$2 billion.
Therefore, in order to solve the dilemma of long drug development time, high investment, high risk, and slow return, AI began to slowly enter the vision of many pharmaceutical companies. Especially since 2018, artificial intelligence (AI) has been on the rise in the medical field. As early as January 2018, AI pharmaceutical company Jingtai Technology completed a series B financing of about US$15 million.
In February 2018, Roche announced that it plans to acquire Flatiron Health, an oncology big data company, for $1.9 billion. After a lapse of 4 months, it also reached a merger agreement with Foundation Medicine, a cancer genetic testing company. In this regard, relevant persons from Roche said that this is an important step in the company's future personalized medical strategy. Standardized real-world data sources and modeling capabilities are essential to accelerate the development of cancer treatment technologies and new drugs.
In April 2019, Gilead and insitro also announced a strategic cooperation to develop treatments for patients with non-alcoholic steatohepatitis. The two companies signed a three-year cooperation agreement worth $1 billion. Through the agreement, Gilead will use insitro's artificial intelligence platform to create disease models for non-alcoholic steatohepatitis, develop treatment options, and discover targets that are helpful for clinical progression and disease regression.
From the above, it can be found that more and more pharmaceutical companies are highly optimistic about AI+ pharmaceuticals and are increasing their layout. Driven by these pharmaceutical giants, the pharmaceutical industry is ushering in the AI-driven "Pharma 2.0" era. However, it is worth noting that due to the complexity of biology, no products related to AI pharmacy have been directly approved for release. The potential of AI in drug development in the pharmaceutical industry still has many obstacles to cross. Now its main role is to assist, including drug mining, deep learning algorithm to analyze data and predict the effectiveness of new drugs, etc., but this is only a preliminary result of AI's entry into the pharmaceutical industry.
In fact, AI may reduce the cost and increase the efficiency of drug research and development, which is causing more and more pharmaceutical companies to choose to increase their costs. You can get a glimpse of the layout for a few years. In general, the attention of capital and the entrance of giants have made AI drug discovery high hopes. Although it has not yet achieved much results, its development prospects have been generally optimistic about the industry.
