The Breakthrough Was There All Along: Rethinking “Undruggable” Targets Through New Ways of Seeing

If you’ve ever played The New York Times game Connections, you know the feeling. You’re staring at a grid of words, knowing the solution is there, but unable to see how the pieces fit together. All you can do is work with the words in front of you. There are no extra clues, no new information coming. The only option is to shuffle, to look at the same information in a different arrangement until patterns begin to appear. 

Nothing about the problem changes. Then something about how you see it does. 

This pattern of reframing is a familiar truth in scientific research as well. In a recent NPR podcast, “The Medical Matchmaking Machine,” Radiolab explores this idea through a deeply human story. The episode features Dr. David Fajgenbaum, who survived a rare, life-threatening illness and came away with a new realization about the limits of how existing knowledge was being connected. By systematically reexamining existing data and research through a new lens, he was able to identify life-saving connections for his own disease. Through his nonprofit research organization Every Cure, Fajgenbaum then began applying the same approach more broadly across diseases.  

In many cases, potential treatments already exist, but they are buried in data, scattered across studies, or confined to discovery pathways that make connections difficult to see. 

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Augmenting Human Capabilities with AI Tools

We hear a lot of stories about AI tools helping people complete tasks more quickly, or automating menial or redundant tasks. However, Promega isn’t just interested in speeding things up. We’re focused on leveraging AI tools to help us do things better. All over the organization, employees are leveraging large language models (LLMs) and machine learning systems to accomplish things that weren’t possible before, or to make their work more effective against their goals.

Three employees shared their recent successes, including strengthening supervisory skills, scaling up production processes and training new team members. Each of these examples uses AI in a unique way, while still elevating human expertise, creativity and decision-making.

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How Artificial Intelligence Revolutionized the 2024 Paris Olympics

At the end of July, many people across the globe were preparing to tune into the two-week, 2024 Olympic Games in Paris, France. The Olympics were slated to feature several high-profile athletes—including Simone Biles (USA, artistic gymnastics), Eluid Kipchoge (Kenya, marathon) and Marta Vieira da Silva (Brazil, football). However, in the lead-up to the Games, the International Olympic Committee (IOC) focused on a secondary player: Artificial Intelligence (AI). The IOC laid out an ambitious AI agenda aimed to enhance athlete performance, ensure fairness and optimize operations. The 2024 Paris Olympics represent a significant leap forward in integrating AI into the world of sports.

Together, we can unlock AI’s full potential to promote solidarity, further digitalization, improve sustainability and resilience, and reinforce the role of sport in society.” – Olympic AI Agenda

Here, we explore several applications of AI in the 2024 Paris Olympics.

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Can AI Replace High-Throughput Screens for Drug Discovery?

This image was created with the assistance of AI

For decades, pharmaceutical companies have relied on high-throughput screening (HTS) as the first step in the drug discovery process. After an initial screening of thousands of compounds, scientists select a smaller list of candidate drugs that is then used for further downstream testing. A major limitation to HTS, however, is the need to synthesize all compounds used in the screen—the compounds need to physically exist to be tested. This significantly limits the number of compounds that can be tested, hindering the discovery of new drugs.

What if we could test compounds even before they are synthesized?

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