Google, Nvidia, and Microsoft are all making efforts. AI drug research and development is a market worth hundreds of billions of dollars!

Through AI models, you can train on hundreds of millions of different protein sequences and their underlying structures, thereby completely simulating proteins and accelerating the process of drug development.

Through the AI ​​model, you can train on hundreds of millions of different protein sequences and their underlying structures, thereby completely simulating proteins and accelerating the process of drug development.
Author of this article: Li Xiaoyin Source: Hard AI More and more technology giants are beginning to increase their bets on AI medical care.
Overnight. AlphaFold, the AI ​​drug development model owned by Google DeepMind and sister company Isomorphic Labs, officially announced a major upgrade. It is said that the latest version AlphaFold 3 can predict the structure of biomolecules such as proteins, DNA, and RNA and how they interact.
Alphabet and Google CEO Sundar Pichai said that currently, more than 1.8 million researchers have used AlphaFold for protein prediction in vaccine development, cancer treatment and other research work.
In an interview, Demis Hassabis, CEO of Isomorphic Labs, said that artificial intelligence systems have the potential to revolutionize medicine and create huge commercial value: I hope to achieve both of these things with Isomorphic: build a business worth hundreds of billions of dollars. I think It has the potential to bring incredible benefits to society and humanity at the same time.
Google is not the only one eyeing this track.
Currently, almost all AI technology giants have shown interest in the biomedical field. Microsoft, Amazon and even Salesforce are also developing protein generation projects.
Recently, Kimberly Powell, Vice President of NVIDIA Healthcare, said in an interview with the media that healthcare will become NVIDIA\’s next multi-billion dollar business. NVIDIA\’s goal is to provide chips, cloud infrastructure and other services to more biotechnology companies. tool.
The next cutting-edge application of AI technology? Nvidia founder and CEO Jensen Huang has repeatedly emphasized that digital biology will be the next amazing disruptive technology.
As he said, at the 2024 GTC conference held by NVIDIA in March this year, medical health was still one of the highlights. Conference activities related to life sciences ranked first among all industries.
In the past two years, Nventures, the venture capital arm of Nvidia\’s AI drug research and development platform BioNeMo, has invested most of its money in drug research and development projects.
Data shows that 7 of Nventures’ 19 investment transactions were invested in AI drug research and development startups.
Powell explained: Computers have already assisted the design industry in creating the first $2 trillion chip company. Why not help build the next trillion-dollar pharmaceutical company? Several other technology giants are also investing in drug research and development.
In the last year alone, Salesforce launched a large AI model for protein generation, ProGen. Microsoft released a similar open source model, EvoDiff. Amazon also released a protein folding tool for its AWS machine learning platform SageMaker. According to reports, even ByteDance Also recruiting for scientific and drug design teams.
This makes people ask: What is the medical value of AI technology? Take the protein field that AlphaFold focuses on as an example.
Protein is an important component of human cell tissue. It is also the main carrier of life activities.
Each protein is made up of a series of amino acids. The interaction between these amino acids and the external environment determines how the protein folds – which determines its final shape. The shape of the protein is the basis for its function.
Therefore, the most valuable point for technology companies is that if the shape of a protein can be predicted based on its amino acid sequence, it can be applied to drug research and development, crop improvement, biodegradable plastics and other major fields.
The emergence of AI has directly pushed this work into a breakthrough moment.
Through the AI ​​model, it is possible to train on hundreds of millions of different protein sequences and their underlying structures, thus completely simulating proteins, eliminating the need for expensive molecular dynamics simulation calculations.
In media interviews, executives from Google DeepMind and Nvidia both said that the large amount of training data available, the explosion of computing resources and the advancement of AI algorithms. These three factors have jointly stimulated the potential of AI in drug research and development.
Powell said: For the first time, these three elements have come together. This was not possible five years ago.
This also stimulates investment enthusiasm.
According to Pitchbook data, there have been 281 venture capital deals in global AI drug research and development startups since 2021, with investment volume reaching US$7.7 billion.
The amount of data is a major bottleneck. However, the process of completely simulating proteins through large AI models requires extremely high computing power. Sufficient amount of training data is still a major bottleneck.
Anna Marie Wagner, head of AI at synthetic biology company Ginkgo Bioworks, said that new basic models like GPT rely on reinforcement learning. It is a learning process that imitates humans\’ repeated training to achieve goals. It relies more on high-quality massive data .
Pushmeet Kohli, Vice President of Science at DeepMind, intuitively described the pain points of data volume: garbage in, garbage out.
Moreover, although there is great potential in applying AI to drug research and development, there is still a long way to go before it can truly enter the pharmaceutical market.
According to reports, the U.S. Food and Drug Administration (FDA) has so far approved clinical trials of more than 100 drug candidates developed using AI or machine learning. But it may take years to reach the market.
This article comes from the WeChat public account Hard AI. For more cutting-edge AI information, please go here] article_adlist–> The market is risky. Investments need to be cautious.
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