Google DeepMind Could Be the First AI Company to Design Drugs $300 Million Less Than Traditional Way

By Thea Felicity

May 05 2024, 12:49 PM EDT

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In 2021, Google DeepMind's AI unit had a breakthrough with its AlphaFold protein structure prediction system. This milestone, often dubbed "the AlphaFold moment," could become the start of artificial intelligence in pharmaceutical innovation.

At the forefront of this is Eli Lilly, a pharmaceutical company embracing artificial intelligence (AI) to accelerate its drug discovery efforts, as reported by CNBC.

Through generative AI algorithms, Lilly scientists can rapidly explore millions of molecules much faster than traditional laboratory synthesis. AI can also propose unconventional molecule designs that are not present in Lilly's existing molecular database.

Google DeepMind's AI Tool For Healthcare

With AlphaFold's success, experts at Lilly and Nvidia anticipate that within a few years, AI will not only propose new drugs but also ones that humans could not have conceived of themselves.

In detail, this digitization of biology can be similar to the mechanisms behind ChatGPT. As ChatGPT continues improving, generating coherent sentences based on its language understanding, AI models can now simulate biological behavior, predicting how molecules might interact and offering novel drug designs that traditional empirical methods might overlook.

AI has the potential to reduce costs by up to $300 million by streamlining the drug development process. Recent studies, including research by Amgen, have shown that AI can slash the time required for drug discovery from years to months.

The probability of success in clinical trials can skyrocket from 50% to 90%, a game-changing improvement that could save millions in trial costs.

While significant strides have been made in using AI to design drugs, there are still hurdles to overcome. One major challenge is ensuring that the molecules generated by AI are effective and safe when tested in human trials.

The exact timeline for releasing AI-generated drugs is uncertain and depends on regulatory approval, further advancements in AI technology, and successful outcomes in human trials.