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Novartis empowers scientists with AI to speed the discovery and development of breakthrough medicines

“Normally, they do this manually, reading through all of these documents to find out what is relevant to the question they have in mind,” Finelli says.

“Here, AI can actually help to do this in a few clicks and bring the relevant information back to the user for further use, informing them how to design future experiments to find new ways to create a formulation for a new drug,” Finelli adds.

Novartis researchers also are leveraging Microsoft Azure in their work.

A Novartis employee types on a laptop keyboard.
By using AI, researchers now can simulate thousands of experiments simultaneously.

Eventually, scientists at Novartis aim to use computer models to help predict promising molecular structures or to reveal which experiments might be most useful in testing, maintaining quality while shortcutting a testing process that now can take years.

“Now you can do 10,000 experiments simultaneously, get the results, then use those to design the next 10,000 experiments,” Bishop says.

“So the revolution is beginning to unfold. Deep learning is completely changing the way we think about simulating physical systems – it might be (simulating) two galaxies colliding or weather systems or the climate. And it might be small molecules binding with proteins – in other words, the whole process of how drugs work,” Bishop adds.

To conduct its molecular simulations, Novartis relies on expertise provided by the Microsoft Research Lab in Cambridge and, in smaller ways, by ongoing work occurring at Microsoft Research Labs in Amsterdam, Beijing and Redmond, Washington, Bishop says.

But at the center of all that discovery, humans continue to be the most vital engine.

Five Novartis scientists working a lab benches, as scene from the outside through a window.
Lab workers at Novartis.

As part of its strategic partnership with Microsoft, Novartis is bringing AI to the desktop of every company associate. At Novartis, they call this “the enablement of citizen data scientists.”

“Business is becoming increasingly data driven. The way I see it, one needs to embed AI-based tools – small engines of AI – into every aspect of an organization’s operation, so a person who is not necessarily a data scientist can have higher-quality, faster decision making,” Ebadollahi says.

Shahram Ebadollahi poses in a photo from the chest up, wearing a dark blue blazer over a light blue shirt.
Shahram Ebadollahi.

This rising concept, also known as the democratization of AI, gives people the ability to use AI to tap into the wealth of data available and derive novel insights and discover breakthrough treatments that improve and extend peoples’ lives.

“That is why we are even doing this work, that is the higher purpose,” Ebadollahi says. “At Novartis, we are impacting human lives through the medicines that we develop. You can’t be a data scientist or a machine learning expert and not have that in the back of your mind every day.”

For Ebadollahi, who spends much of his time focusing on the deep intricacies of datasets and machine learning, there’s always mental space for loved ones, family members and friends who are dealing with health problems.

Those are the human touchpoints, he says, that help to energize his mission, that clarify what every workday should really be about. It is their faces that sometimes enter his thoughts alongside the latest tech responsibilities on his plate.

“At a medicines company, you hear about the ailments, the diseases for which these fantastic scientists, biologists and chemists are in search of the drug,” Ebadollahi says. “It’s very, very present in the atmosphere.

“In the thick of the work, in the day-to-day of business, you might get lost in the noise a little bit, but it’s good to step back and look at why you are doing what you’re doing. And when I do, those are the pictures I see – the faces of loved ones come to my mind.”

Top photo: A Novartis scientist reaches for a vial inside a lab hood. (All photos courtesy of Novartis)



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