Artificial intelligence is no longer a futuristic promise in medicine. It is already helping drug developers design new treatments faster and make smarter decisions about which drugs to advance, according to the CEO of one of the world’s largest pharmaceutical companies.
AstraZeneca CEO Pascal Soriot said the company is using AI to improve productivity across the entire drug development process. “The value of AI in our industry is productivity improvement,” Soriot said. “In the way you design a new medicine, a new drug, you can actually do it faster, do it smarter.” The comments come as investors and researchers alike ask whether massive spending on AI is delivering real results in healthcare. Soriot says the answer is yes, pointing to practical applications already in use.
One of the most promising uses of AI is in the earliest stages of drug discovery. Soriot explained that AI can help scientists identify new biological targets for medicines and then optimize the design of potential molecules. “You can come up with new targets, but also you can optimize your molecule, remove what you think is going to be potential side effects from the molecule, and AI helps you do this,” he said. This means AI can act like a highly skilled assistant, sifting through vast amounts of data to spot patterns and possibilities that humans might miss.
AI is also helping AstraZeneca make better decisions about which drugs to push forward into expensive late-stage trials. Through a partnership with Tempus AI, the company has developed an AI agent that pulls together clinical data, laboratory data, and other information. “We have developed an agent that takes all this data together and helps us predict the probability of success of a Phase 3 trial,” Soriot said. Given that a single late-stage trial can cost $300 million to $500 million, even a small increase in the odds of success translates into enormous savings and faster delivery of effective treatments to patients.
Looking ahead, Soriot emphasized that the productivity gains from AI are “enormous” when applied to the right challenges. As AI models continue to improve and integrate more types of data, the potential to accelerate drug development and reduce costs will only grow. For patients, this could mean more new medicines reaching the clinic faster, with fewer side effects and a higher chance of success. The future of drug development, it seems, is being shaped not just in laboratories but through the intelligent analysis of data.