Nvidia’s Kimberly Powell is applying AI to expedite drug discovery
Bringing a new drug to market usually requires a decade-long, multibillion-dollar journey, with a high failure rate in the clinical trial phase. Nvidia’s Kimberly Powell is at the center of a major industry effort to apply AI to the challenge.“If you look at the history of drug discovery, we’ve been kind of circling around the same targets for a long time, and we’ve largely exhausted the drugs for those targets,” she says. A “target” is a biological molecule, often a protein, that’s causing a disease. But human biology is extraordinarily complex, and many diseases are likely caused by multiple targets.“That’s why cancer is so hard,” says Powell. “Because it’s many things going wrong in concert that actually cause cancer and cause different people to respond to cancer differently.”Nvidia, which in July became the first publicly traded company to cross $4 trillion in market capitalization, is the primary provider of the chips and infrastructure that power large AI models, both within the tech companies developing the models and the far larger number of businesses relying on them. New generative AI models are quite capable of encoding and generating words, numbers, images, and computer code. But much of the work in the healthcare space involves specialized data sets, including DNA and protein structures. The sheer number of molecule combinations is mind-bogglingly big, straining the capacity of language models. Nvidia is customizing its hardware and software to work in that world.“[W]e have to do a bunch of really intricate data science work to . . . take this method and apply it to these crazy data domains,” Powell says. “We’re going from language and words that are just short little sequences to something that’s 3 billion [characters] long.”Powell, who was recruited by Nvidia to jump-start its investment in healthcare 17 years ago, manages the company’s relationships with healthcare giants and startups, trying to translate their business and research problems into computational solutions. Among those partners are 5,000 or so startups participating in Nvidia’s Inception accelerator program.“I spend a ton of my time talking to the disrupters,” she explains. “Because they’re really thinking about what [AI computing] needs to be possible in two to three years’ time.”This profile is part of Fast Company’s AI 20 for 2025, our roundup spotlighting 20 of AI’s most innovative technologists, entrepreneurs, corporate leaders, and creative thinkers.
Powell highlights a critical issue in traditional drug discovery: the industry has largely exhausted the possibilities of targeting the same biological molecules, or "targets," that cause diseases. She emphasizes the complexity of human biology, noting that many diseases, particularly cancer, are likely caused by multiple targets working in concert, leading to varied responses among individuals.
Nvidia, a leading provider of chips and infrastructure for large AI models and recently the first publicly traded company to surpass a $4 trillion market capitalization, is uniquely positioned to address this challenge. While generative AI models excel at processing and generating various forms of data, including text and images, the healthcare sector requires specialized handling of complex data sets like DNA and protein structures. The sheer scale of possible molecule combinations presents a significant strain on the capacity of existing language models.
To overcome this, Nvidia is customizing its hardware and software to efficiently process and analyze these intricate biological data domains. Powell explains the need for "really intricate data science work" to adapt AI methods to these "crazy data domains," transitioning from processing short sequences of language to handling sequences that are billions of characters long.
Powell, who joined Nvidia 17 years ago to spearhead its healthcare investments, now manages the company's relationships with both established healthcare giants and innovative startups. Her role involves translating their research and business problems into computational solutions, fostering collaboration and innovation. A key aspect of this work involves nurturing the 5,000 or so startups participating in Nvidia's Inception accelerator program.
Powell emphasizes the importance of engaging with these "disrupters," as they are the ones envisioning the future of AI computing in the healthcare space, anticipating the needs and possibilities of the technology within the next two to three years.