Biotechs are applying AI and machine learning to drug development
Published in Artificial Intelligence, Drugs development.
Biotechs are applying AI and machine learning to drug development, potentially creating dozens of new medicines and a $50 billion market over the next decade. Here’s what that means for patients and investors.
For biotechnology companies, much of the traditional process of discovering new drugs is costly guesswork. But a new wave of drug development platforms, enabled by artificial intelligence, is helping companies use vast data sets to quickly identify patient response markers and develop viable drug targets more cheaply and efficiently.
The results could be transformative not just for medical providers and patients suffering from hard-to-treat diseases, but for the biotech sector: Morgan Stanley Research believes that modest improvements in early-stage drug development success rates enabled by the use of artificial intelligence and machine learning could lead to an additional 50 novel therapies over a 10-year period, which could translate to a more than $50 billion opportunity.
“Predictive diagnostics, enhanced by data, present a significant near-term opportunity for the life sciences industry,” says Tejas Savant, who covers life science tools and diagnostics at Morgan Stanley Research. “It’s also likely to resonate with payors, since these trials can generate better outcomes. They can also deliver sizable cost savings by enabling earlier identification and treatment of higher-risk patients.”
Technological advances in recent years have made it easier to capture and store reams of digital patient data. This has resulted in rich troves of genomic data, health records, medical imaging and other patient information that AI platforms can mine to help to develop drugs faster and with greater chance of success in the early stages of creation.