Global AI+ R&D competition!

According to the drug development process, AI technology can be used in multiple directions such as target discovery, early drug development, preclinical experiment design and processing, clinical trials, reuse of existing drugs, information integration and new insight output
.

"10 years, 1 billion US dollars, 10% success rate
.

" It is often used to describe the difficulty and embarrassment of new drugs on the market

At the end of 2020, Deep Pharma Intelligence (DPI), a consulting organization focusing on the pharmaceutical, biotechnology, and healthcare technology industries, released the "2020 Artificial Intelligence Report in the Pharmaceutical Industry", which has evaluated 240 companies and 600 companies that use artificial intelligence to develop new drugs.
Investors who invest in the research and development of AI biomedicine conducted an analysis to show the current development status and challenges of AI in the biomedicine field
.

01 Application: Multi-directional output

01 Application: Multi-directional output

According to the drug development process, AI technology can be used in multiple directions such as target discovery, early drug development, preclinical experiment design and processing, clinical trials, reuse of existing drugs, information integration and new insight output
.

AI has been regarded as a new drug development tool by some large pharmaceutical companies
.

Building models and mining data from unstructured data are considered to be the most disruptive areas of artificial intelligence in drug discovery

In the design and processing of preclinical experiments, AI technology is hoped to be used to reduce the time of the experimental phase, reduce costs and uncertainty
.

Under normal circumstances, AI pharmaceutical companies will find new R&D perspectives by analyzing existing data, and automated sample analysis and machine cloud laboratories have also emerged

In the clinical trial phase, AI technology is expected to have more uses
.

For example, optimizing clinical trial research design; transforming different biomedical and healthcare data streams into computer models that represent individual patients; providing individual patients with the best health interventions to provide large-scale personalized medicine; and analyzing medical records , Find suitable clinical trial patients; Automatically match the clinical trials of cancer patients through personal medical history and genetic analysis; Improve pathological analysis

In the process of searching for new coronary pneumonia treatment options, the pharmaceutical industry has seen the feasibility and importance of reusing existing drugs
.

AI technology has also been used to mine the reuse of existing drugs.

AI technology may still have many challenges in the above links, but AI technology is better than manual in terms of information aggregation and integration
.

At present, AI technology has been used in many important aspects such as analyzing literature, deriving new opinions from thousands of unrelated data sources, improving decision-making, eliminating research blind spots, and identifying blank competition areas

02 Participation: Multi-player entry

02 Participation: Multi-player entry

Whether it is AI technology or the application of AI technology in the pharmaceutical field, it is an area that many countries around the world are vigorously developing
.

From a global perspective, the United States is both a pioneer and a major participant in the AI ​​industry, and its number of AI technology R&D companies ranks first in the world
.

54.

China is the "artificial intelligence superpower" in the Asia-Pacific region.
Although only 8.
4% of the world's AI technology research and development companies are located in the Asia-Pacific region, companies in China account for nearly 30%
.

Domestic governments and investment institutions are also increasing investment in the AI ​​industry.

Europe has always been a market that biopharmaceutical companies cannot ignore, and there have been many transactions on AI drug research and development on such a piece of land
.
Novartis established the Novartis Artificial Intelligence Innovation Lab and selected Microsoft as its strategic partner for artificial intelligence and data science, announcing an important step in reshaping the field of medicine
.
GlaxoSmithKline announced that it has reached an agreement with companies such as Exscientia and Insilicon Medicine to try a new computer modeling system
.
BenevolentAI, the largest AI new drug research and development company in Europe, has also attracted the attention and favor of many pharmaceutical companies, and AstraZeneca, Johnson & Johnson, etc.
have cooperated with them to develop new drugs
.

All this shows that pharmaceutical companies are increasingly turning to AI technology to change the drug discovery process
.

In addition, international pharmaceutical companies such as Bayer, Pfizer, AstraZeneca, Takeda, and GlaxoSmithKline are very optimistic about the application of AI technology in the field of biomedicine
.

Bayer can be said to be one of the most promising companies in the industry.
Its AI partners include Cyclica, Exscientia, Genpact, Schrödinger, Sensyne and many other well-known AI drug discovery companies
.

On August 30, 2018, Cyclica announced that it has joined Bayer’s Grants4Apps program and will cooperate with Bayer to accelerate the deployment of its differentiated drug design (DDD) technology
.
In January 2020, Bayer and Exscientia began a three-year collaboration to identify and optimize the structure of new lead compounds for cardiovascular and tumors
.
In the same month, Bayer and Schrödinger announced a five-year technical cooperation to discover, screen and evaluate synthetic virtual compounds
.

Pfizer's partners in the field of AI technology include Atomwise, Concerto, CytoReason, IBM, Insilicon Medicine, Jingtai Technology and other companies
.

In September 2018, Pfizer signed an evaluation agreement with Atomwise.
Atomwise will look for potential drug candidates for three target proteins for Pfizer
.
In January 2020, Pfizer and Insilicon will cooperate to use Insilicon's machine learning technology and proprietary Pandomics discovery platform to find real-world data for the development of potential therapeutic targets
.

AstraZeneca is also using data and AI technology to better identify drug targets, recruit patients, and better design clinical trials to increase the success rate of drug development
.
AstraZeneca’s partners in the field of AI technology include companies such as Schrödinger, BenevolentAI, Berg, DeepMatter, and Gatehouse Bio
.

In September 2019, AstraZeneca and Schrödinger cooperated, hoping to use Schrödinger's computing platform to improve the design of compounds and explore potential therapeutic drugs
.
In December of the same year, AstraZeneca cooperated with Gatehouse Bio to use its AI platform to explore new drug targets for respiratory diseases and cardiovascular diseases
.

As a Japanese company among the top ten global pharmaceutical companies, Takeda has shown that its investment in this field has surpassed that of other Japanese pharmaceutical companies
.
Its partners in the field of AI technology mainly include Numerate and Recursion
.

In June 2017, Takeda and Numerate reached a multi-year agreement to rely on Numerate's AI-driven platform for active compound discovery, lead compound design and optimization, and compound ADME-T property modeling for its core therapeutic areas
.

GlaxoSmithKline is one of the most active multinational pharmaceutical companies in the AI ​​pharmaceutical field and one of the first companies to create an internal AI department
.
Partners in the field of AI technology are more extensive, including Cloud Pharmaceuticals, Excscientia, Google, Insilico Medicine and other companies
.

The proprietary AI-driven process of drug design and development company Cloud Pharmaceuticals can provide completely novel molecules for drug targets
.
In May 2018, GlaxoSmithKline and Cloud Pharmaceuticals reached a collaboration to use the AI ​​technology platform to design new small molecule drugs
.
In June of the same year, GlaxoSmithKline researchers cooperated with Google researchers to develop new drugs using AI technology
.

03 Transaction: multiple capital investment

03 Transaction: multiple capital investment

In the past two years, the interest of major pharmaceutical companies in AI technology has changed from "worth trying" to "strategic importance.
" This demand has driven the growth of the AI ​​market and also allowed investors to see more opportunities
.
Compared with 2019, the total investment received by AI biotechnology start-ups in 2020 has increased by about 23% to nearly US$1.
9 billion, and surpassed the sum of 2015, 2016 and 2017
.

In terms of the number of investment companies, Google Ventures topped the list with its investment in 13 AI pharmaceutical companies.
Its investment companies include companies such as Alector, BlackThorn Therapeutics, and ZappRx
.

It is worth mentioning that WuXi AppTec entered the top ten with its investment in 7 AI pharmaceutical companies, ranking sixth
.
Its investment companies include Engine Biosciences, Insilico Medicine, Insitro and Schrödinger and other leading companies in the field of Al drug research and development
.

It can be seen from the amount of corporate financing that the industry is accelerating its integration
.
Some artificial intelligence start-up companies have achieved a leading position and have grown in terms of resources and technology, while some companies have fallen behind and have to focus on the segmented service area of ​​drug research and development, and some companies have closed down
.

In September 2020, Jingtai Technology, an AI drug research and development company driven by digitization and intelligence, completed a $319 million Series C financing, setting a record for the highest financing amount in the global AI drug research and development field at that time
.
Many well-known investment institutions such as SoftBank Vision Fund, Sequoia China Fund, CICC Capital, and Tencent are on its investor list
.
The company's co-founder and chairman Wen Shuhao is an adjunct professor at Zhejiang University.
He has 11 years of study, research and work experience at the Chinese Academy of Sciences, the University of California, and the Massachusetts Institute of Technology
.
Jingtai Technology was founded on the campus of Massachusetts Institute of Technology (MIT), headquartered in Shenzhen, with branches in Beijing and Boston
.
The company has a number of computing solid-state R&D technologies for small molecule drugs, including crystal form prediction, single crystal structure analysis, virtual screening of salts and eutectics, as well as solid-state R&D experimental platforms.
At present, it has cooperated with Pfizer, East China Medicine, Xingeyuan and other domestic companies.
More than 70 foreign pharmaceutical companies have reached cooperation to provide drug research and development services
.

In the same month, Recursion Pharmaceuticals, a US AI drug research and development company, received US$239 million in Series D financing, led by Bayer
.
This financing amount ranks second among the top five financing amounts in 2020
.
Founded in 2013, Recursion is a company that hopes to realize the industrialization of drug discovery by integrating technological innovations in the fields of biology, chemistry, automation, data science, and engineering
.
Dr.
Yoshua Bengio, one of the company's scientific directors, was a postdoctoral researcher at MIT and AT&T Bell Labs
.
He and Geoffrey Hinton, a British computer psychologist, and Yann LeCun, the head of Facebook's artificial intelligence laboratory, are known as the "Big Three in the field of deep learning
.
" In April of this year, the company landed on Nasdaq and raised US$502 million
.

In May 2020, Insitro completed $143 million in Series B financing, ranking third among the top five financing amounts in 2020, and this round of financing is less than two years after the establishment of Insitro
.
In 2018, Dr.
Daphne Koller, a well-known scholar at Stanford University, founded Insitro
.
During the nearly 20 years as a professor of computer science at Stanford University, she has published more than 200 papers in top academic journals and has also won the "MacArthur Genius Award", one of the highest cross-disciplinary awards in the United States
.
Insitro's application of AI technology in drug development has well demonstrated the value of AI technology
.
It uses machine learning to perform statistical genetic analysis on populations with deep phenotypic characteristics to discover potential targets and specific patient groups that may provide guidance for clinical development; to discover new targets, and to establish cell-based predictive disease models.
Patient subgroups and drugs to treat liver and central nervous system diseases; use machine learning to design therapies
.
In March of this year, the company completed another US$400 million in financing, surpassing Jingtai's previous financing of US$319 million
.

In August 2020, Atomwise, the leading company discovered by AI small molecules, received US$123 million in Series B financing
.
This round of financing was led by B Capital Group and Saudi public investment fund Sanabil Investments.
Old shareholders DCVC, BV Baidu Ventures, Tencent, Y Combinator, etc.
continued to invest
.
Since its establishment in 2012, Atomwise has been focusing on developing and improving AI-based drug discovery technology, and created the first Convolutional Neural Network (CNN) for drug discovery
.
The company has entered into drug R&D cooperation with large pharmaceutical companies such as Eli Lilly, Bayer, and Hausen, and many emerging biotechnology companies
.

In May 2020, AbCellera completed a US$105 million Series B financing.
Investment companies such as Data Collective Bio and Viking Global Investors participated in this round of financing
.
AbCellera is an AI-driven antibody discovery company.
Its technology platform can screen B cells at the single-cell level by bringing together high-throughput microfluidics technology, machine vision, and artificial intelligence technology, thereby increasing the speed of discovering antibody candidate therapies And efficiency
.
The candidate antibody therapy LY-CoV555 jointly developed by it and Eli Lilly is one of the first new coronavirus neutralizing antibody therapies to enter clinical trials
.
At the end of 2020, the company landed on Nasdaq and raised US$556 million
.

04 Challenge: Talent and data

04 Challenge: Talent and data

Although the application of AI technology in the biopharmaceutical field has made great progress and has shown many potentials, it also faces many challenges
.

One is the lack of talents
.
The shortage of AI technical talents is still a serious challenge facing the global biopharmaceutical industry
.
Although many international pharmaceutical companies invest a lot of money to recruit artificial intelligence experts, most of them are still absorbed by large technology companies such as Google, Amazon, Alibaba, Tencent, and Baidu
.
From the perspective of global talent flow, most AI technical talents are still gathered in the United States
.
The "Artificial Intelligence Index 2021 Annual Report" released by the Institute of Artificial Intelligence at Stanford University shows that in 2019, 65% of the Ph.
Ds graduating from artificial intelligence majors in North America entered the industry, and 64.
3% of them were international students
.
And 81.
8% of international students chose to stay in the United States to work
.

The second is the lack of available high-quality data, which is still a hurdle that AI technology needs to overcome
.
The deep learning model is considered to be the most promising application of AI technology in the biopharmaceutical field, but the foundation of deep learning is a large amount of available and high-quality data
.
At present, the data in public databases cannot be well modeled, and high-quality data is scarce and difficult to obtain
.

The third is the lack of ethical consensus and complete regulatory requirements
.
At present, AI technology still lacks benchmarks and consensus in ethics and supervision, and the application in the pharmaceutical industry is also true
.
Although some organizations have formulated a series of regulations on the ethics of AI technology, there is still a lack of universally available measurement or evaluation benchmarks
.
In addition, there is still a lack of corresponding legal regulations in terms of patents and supervision of the results of AI technology
.