Breakthrough Medication from Google’s New AI Tools

AI-Powered Medicine

The AI arms race has shaken up the landscape of the tech sector and many other industries connected to it. For a time, it appeared the quintessential tech giant Google (GOOGL) was falling behind competitors like ChatGPT-developer OpenAI and its principal backer Microsoft (MSFT).

But Google has since clapped back with a seemingly non-stop stream of headline-grabbing AI reveals, including the integration of its Bard chatbot into its ubiquitous search engine. The tech goliath is following up with two tools tailored to an entirely different industry: biopharma.

Entering a New Market

Google announced two biopharma-focused AI-powered tools: the Target and Lead Identification Suite and the Multiomics Suite.

The Target and Lead Identification Suite aims to speed up drug development by using AI to predict and understand protein structure. This could help researchers identify biological targets to build treatments around — a process that currently takes around 12 months.

Meanwhile, the Multiomics Suite will help researchers analyze massive amounts of genomic data in order to advance precision medicine. By providing researchers with the infrastructure to analyze enormous data sets, the Multiomics Suite has reportedly already proved useful to genomic scientists working to reverse extinction.

With these tools, Google hopes to get a leg up in both the AI arms race and the pharmaceutical industry. According to industry insiders, the latter could be worth trillions in the coming years.

Prescribing Progress

Google’s new tools will address a major problem in the biopharma industry: the cost — in time and money — of bringing a new medicine to the market.

It typically costs up to $2 billion to discover, research, and create a new drug. From there, it can take 10 to 15 years to properly research and test the new drug to ensure it’s up to FDA standards. Even then, the FDA only approves 16% of medicines that reach clinical trials.

These new AI tools could drastically reduce the time and money involved to create a new treatment — and hopefully increase its efficacy as well. The implications are vast, and if they work as intended, you may see results in the form of breakthrough medicines far sooner than you realize.

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