Connecting the Data
A new AI platform aims to harness scientific data for improved drug development
James Strachan | | Quick Read
Companies are increasingly investing in AI, and machine and deep learning to open up new routes to innovation, but the technology is new and the data available in life sciences is vast – and many are not seeing the insights from AI that they expected. Elsevier has developed a new AI platform called Entellect that is specifically designed to cope with scientific data. The platform can contextualize and connect drug, target, and disease data. We speak with Tim Miller, VP Elsevier, Life Sciences Platform Solutions, to find out more.
How can using data more efficiently aid drug development?
Every drug development program generates massive amounts of data – and hidden within that data can be a new clue that might start a new drug program, or halt a drug program otherwise doomed to fail expensively. But so much of a data scientist’s time is currently taken up with tasks such as cleansing, integrating and formatting data. Simply making data management more efficient could make a huge difference. Data is the lifeblood of life sciences R&D today and removing the obstacles to using it is critical in supporting the industry to deliver positive outcomes for patients. Whether realizing the full potential of precision medicine, identifying drug candidates for repurposing in rare disease treatment, or analyzing the safety and efficacy profiles of compounds in early R&D – the potential benefits through the use of deep learning platforms are significant.
What are the challenges of using AI in life sciences?
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