In 2021, the pharma industry invested over $600 billion in R&D, M&A and licensing in search of the next life-changing and market-leading products. With a patent cliff in 2028 looming, the race is on to identify the assets that will drive the next wave of success.

But there’s a problem. Our analysis found that just 20% of marketed drugs generate 90% of commercial returns, suggesting that pharma’s investment in novel molecules may not always deliver the returns expected. There may be no magic formula for building accurate forecast and valuation models, but there are new approaches that close the gap.

Enter, machine learning. 



How the early-stage information gap limits R&D productivity
Reduced productivity and the future R&D landscape
How to fill the gap with machine learning