, I found a great one in a recent discussion with Vipin Gopal, chief data and analytics officer at Eli Lilly, the $28.31 billion pharmaceutical company. It’s practical because it demonstrates how the recent advancements in machine learning, specifically deep learning’s success at analyzing images, can be shown to quantifiably improve a traditional process, in this case in manufacturing.Gopal reported that inspecting the quality of the syringes Lilly produces used to be done manually.
Another example of improving the efficiency and quality of operations Gopal mentioned was related to the core business of the company, drug development. It usually takes about ten years and $2.5 billion to bring a new drug to market. Aiming to cut down drug development time wherever possible, “we looked at various decisions that are made as part of the operationalization of clinical trials,” says Gopal. Which sites to work with in conducting the clinical trial proved to be a critical decision.
The third example from Lilly’s experience with AI has to do with the first step its research organization takes in developing a new drug. “AI enables model-driven drug discovery, to narrow down the funnel to a handful of molecules,” says Gopal. The data from previous drug discovery efforts and from previous clinical trials is analyzed by the new AI tools, speeding up to process of identifying the right molecules from which to create the required compounds.
With the new AI tools—and other initiatives—Lilly has managed to cut down drug development time by about three years. “Human intuition plus the power of machine learning coming together to produce the best solution,” Gopal sums up the experience.