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Why does AI pharmaceutics trigger the "grab battle" with an annual salary of one million US dollars?

 Last Update: 2021-03-30
 Source: Internet
 Author: User

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AI is the killer to "break" the anti-Moore's Law in the pharmaceutical industry.
At the stage when domestic innovative drugs are not mature enough, AI-enabled Chinese innovative drugs may usher in the opportunity of overtaking in a curve, just like the immaturity of China's retail industry, giving Chinese e-commerce an opportunity to become a model for the world.

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It depends on how popular AI pharma is, it depends on how ruthless the company is to dig people.

It depends on how popular AI pharma is, it depends on how ruthless the company is to dig people.
At the end of 2019, Huawei began to vigorously recruit genome R&D algorithm engineers and drug R&D algorithm engineers, with a maximum annual salary of over one million.
By the end of 2020, ByteDance recruited bioinformatics engineers with a monthly salary of about 40k; and Baidu was the most ruthless.
After the high-profile official announced that the life science company Baitu Shengke, a subsidiary of the company, used a sky-high annual salary of $1 million to hire people.

Compared with the high-paying digging of giants, investment institutions are probably the only ones that are worse.

A well-known capital's 2021 medical recruitment plan clearly states that the basic annual salary and performance of medical analysts in the first year can be as high as 1 million US dollars.
Doctors in biomedicine have almost become the scarce "species" of the year.
Compared with the high-paying digging of giants, investment institutions are probably the only ones that are worse.
A well-known capital's 2021 medical recruitment plan clearly states that the basic annual salary and performance of medical analysts in the first year can be as high as 1 million US dollars.

Which links are suitable for AI, and what technical solutions are there if AI does not work?

In terms of business model, do you want to participate in the "gold rush" after you have completed the technical "tools"?

In the early stage, where is the AI pharmaceutical bubble?

Is the AI Pharmaceutical Conference an opportunity for China's innovative drugs to overtake on a curve?

After the giants run into the market, what is the industry pattern?

Why is AI pharmacy the general trend? Why is the outbreak now?

Why is AI pharmacy the general trend? Why is the outbreak now? AI

Which links are suitable for AI, and what technical solutions are there if AI does not work?

In terms of business model, do you want to participate in the "gold rush" after you have completed the technical "tools"?

In the early stage, where is the AI pharmaceutical bubble?

Is the AI Pharmaceutical Conference an opportunity for China's innovative drugs to overtake on a curve?

After the giants run into the market, what is the industry pattern?

In order to understand the development trend of the industry, combine the wonderful sharing of the following 6 entrepreneurs and 2 investors.

Ma Rui, Executive Director of Fengrui Capital
Gao Jiankai, Assistant Partner of Lightspeed China
Lai Caida, Founder & CEO of Jitai Pharmaceutical
Commadison founder & CEO Wan Xiaobo
Deng Daiguo, Founder & CEO of Fermion Technology
Xia Ning, Founder & CEO of Zhihua Technology
Yiyao Technology CEO Xie Zhengwei
Wen Wen, Founder & CEO of Huanyi Biotech

Ma Rui, Executive Director of Fengrui Capital

Gao Jiankai, Assistant Partner of Lightspeed China

Lai Caida, Founder & CEO of Jitai Pharmaceutical

Commadison founder & CEO Wan Xiaobo

Deng Daiguo, Founder & CEO of Fermion Technology

Xia Ning, Founder & CEO of Zhihua Technology

Yiyao Technology CEO Xie Zhengwei

Wen Wen, Founder & CEO of Huanyi Biotech

Try to answer the above questions and summarize the future development opportunities of some AI pharmaceuticals.

544px;white-space:normal;color:#A0A0A0;background-color:#FFFFFF;text-align:center;font-family:-apple-system-font, BlinkMacSystemFont, Arial, sans-serif;overflow-wrap:break-word ;">A track that no one dares to missA track that no one dares to missA track that no one dares to miss

The pharmaceutical industry is a rare "wonder".

The number of new drugs on the market obtained from its investment of US$1 billion is halved every 9 years.
This kind of anti-Moore's Law industry is almost hard to find a second one.
The pharmaceutical industry is a rare "wonder".
The number of new drugs on the market obtained from its investment of US$1 billion is halved every 9 years.
This kind of anti-Moore's Law industry is almost hard to find a second one.
The pharmaceutical industry is a rare "wonder".
The number of new drugs on the market obtained from its investment of US$1 billion is halved every 9 years.
This kind of anti-Moore's Law industry is almost hard to find a second one.
Under the quagmire of "anti-Moore's Law", the development of new drugs has naturally become "unthankful".
In 2017, the return on investment of the world's top 12 biopharmaceutical giants in R&D was only 3.
2%, the lowest level in eight years.
The average investment of a single NME (new molecular entity) has also risen from US$300 million in 1995 to US$1.
3 billion in 2020.
Under the quagmire of "anti-Moore's Law", the development of new drugs has naturally become "unthankful".
In 2017, the return on investment of the world's top 12 biopharmaceutical giants in R&D was only 3.
2%, the lowest level in eight years.
The average investment of a single NME (new molecular entity) has also risen from US$300 million in 1995 to US$1.
3 billion in 2020.
Under the quagmire of "anti-Moore's Law", the development of new drugs has naturally become "unthankful".
In 2017, the return on investment of the world's top 12 biopharmaceutical giants in R&D was only 3.
2%, the lowest level in eight years.
The average investment of a single NME (new molecular entity) has also risen from US$300 million in 1995 to US$1.
3 billion in 2020.
This "abnormal" phenomenon has caused the industry to start thinking, hasn't it found the most scientific method to discover new drugs?This "abnormal" phenomenon has caused the industry to start thinking, hasn't it found the most scientific method to discover new drugs?To put it simply, the process of new drug discovery is generally to determine the target of a certain disease first, and the target is equivalent to "lock", we need to design and screen the most suitable molecule among the 10^60 drug molecule possibilities Use as a "key" to unlock.
You must know that there are only 10^54 atoms in the solar system.
With such a large number of drug molecules, traditional methods do rely on scientists to design and verify them "with bare hands.
"To put it simply, the process of new drug discovery is generally to determine the target of a certain disease first, and the target is equivalent to "lock", we need to design and screen the most suitable molecule among the 10^60 drug molecule possibilities Use as a "key" to unlock.
You must know that there are only 10^54 atoms in the solar system.
With such a large number of drug molecules, traditional methods do rely on scientists to design and verify them "with bare hands.
"Similarly, after screening a good molecule, organic synthesis and molecular improvement are required to form a real drug, and these still rely on the artificial attempts of scientists, so the chance of success is extremely high, and there is no scale effect.
Similarly, after screening a good molecule, organic synthesis and molecular improvement are required to form a real drug, and these still rely on the artificial attempts of scientists, so the chance of success is extremely high, and there is no scale effect.
Similarly, after screening a good molecule, organic synthesis and molecular improvement are required to form a real drug, and these still rely on the artificial attempts of scientists, so the chance of success is extremely high, and there is no scale effect.

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Wen Wen, the founder of Huanyi Biotech, mentioned that more than 190 new drugs successfully approved by the FDA in the past 10 years were basically developed by more than 120 companies.

Even the top 10 pharmaceutical companies in the world rely on the R&D of acquisitions of small pharmaceutical companies.
The results, "fully show that the efficiency of drug research and development is extremely low, and the effect of scale has not been formed.
"Wen Wen, the founder of Huanyi Biotech, mentioned that more than 190 new drugs successfully approved by the FDA in the past 10 years were basically developed by more than 120 companies.
Even the top 10 pharmaceutical companies in the world rely on the R&D of acquisitions of small pharmaceutical companies.
The results, “fully show that the efficiency of drug research and development is extremely low, and the scale effect is not formed.
” Wen Wen, the founder of Huanyi Biotech, mentioned that more than 190 new drugs successfully approved by the FDA in the past 10 years were basically developed by more than 120 companies.
It comes out that even the top 10 pharmaceutical companies in the world rely on the research and development results of the acquisition of small pharmaceutical companies.
“It fully shows that the efficiency of drug research and development is extremely low and it has failed to form a scale effect.
”Essentially, the drug discovery process is a data and engineering issue.
In stark contrast to the anti-Moore's law of pharmaceuticals is "computing power.
" AI, which is blooming in many fields of autonomous driving, has a higher computing power and lower marginal cost as it is invested.
It is almost natural for AI and other calculations to cut into pharmaceuticals.
Essentially, the drug discovery process is a data and engineering issue.
In stark contrast to the anti-Moore's law of pharmaceuticals is "computing power.
" AI, which is blooming in many fields of autonomous driving, has a higher computing power and lower marginal cost as it is invested.

"We all know that there is a lot of data in the field of medicine.
Using AI to do things may innovate something, but the cycle of medicine is too long and the failure rate is also high.
No one can prove that AI can really make a difference, and there is nothing.
People dare to pay for the result.
" An investor from a first-line investment institution talked about his initial hesitation on the track.
"We all know that there is a lot of data in the field of medicine.
Using AI to do things may innovate something, but the cycle of medicine is too long and the failure rate is also high.
No one can prove that AI can really make a difference, and there is nothing.
People dare to pay for the result.
" An investor from a first-line investment institution talked about his initial hesitation on the track. The same is true for Deng Daiguo, the founder of Fermion Technology, when he comes into contact with investors.
First, because of the strong interdisciplinary nature, the communication between entrepreneurs and investors is much more difficult and complicated than other vertical industries; second, good business models have not fully run.
When it came out, no one was sure.
The same is true for Deng Daiguo, the founder of Fermion Technology, when he comes into contact with investors.
First, because of the strong interdisciplinary nature, the communication between entrepreneurs and investors is much more difficult and complicated than other vertical industries; second, good business models have not fully run.
When it came out, no one was sure.
The same is true for Deng Daiguo, the founder of Fermion Technology, when he comes into contact with investors.
First, because of the strong interdisciplinary nature, the communication between entrepreneurs and investors is much more difficult and complicated than other vertical industries; second, good business models have not fully run.
When it came out, no one was sure.
Because of the strong interdisciplinary nature,But the time has come to 2020, and a multi-dimensional landmark event broke the deadlock.
But the time has come to 2020, and a multi-dimensional landmark event broke the deadlock.
First of all, under the epidemic situation , the speed of research and development of new coronavirus vaccines has attracted much attention, which undoubtedly pushed the industry 's attention to the technical efficiency enhancement methods of drug research and development to a high point.
The first is under the epidemic , above all under the epidemic , research and development speed of the new virus vaccines crown very popular attention, which will undoubtedly industry for drug discovery technology , Improve Efficiency attention means pushed to a high point.
Improve efficiencySome global leading pharmaceutical companies are also more actively embracing AI pharmaceutical companies.
According to public statistics (see the figure below for details), Novartis, Bayer, Johnson & Johnson and other companies have already cooperated with AI pharmaceutical companies, and many transaction amounts are more than 100 million US dollars.
Some global leading pharmaceutical companies are also more actively embracing AI pharmaceutical companies.
According to public statistics (see the figure below for details), Novartis, Bayer, Johnson & Johnson and other companies have already cooperated with AI pharmaceutical companies, and many transaction amounts are more than 100 million US dollars.

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