AI in Drug Discovery Has Great Potential â But Also Significant Barriers
Itâs an exciting new time for drug discovery and development as whole new worlds of data have opened up. Wearable devices that collect personal health readings and services such as consumer genetic testing are making loads of potentially insightful data available. But all that potential comes with new challenges â namely, how do we process and analyze all this new data?
Artificial intelligence presents a timely answer to this problem. AI has an array of useful applications in pharma, whether itâs identifying candidates for drug repurposing or assessing safety issues in early-stage discovery. Once deployed to help in these capacities, AI and machine learning can speed up costly, time-consuming processes â freeing up researchersâ time to focus on the actual science.
But there are many real barriers to making all of these possibilities into realities. The recent announcement that IBM is stopping development and sales of Watson for Drug Discovery, a product that was using IBMâs Watson AI software, points to this fact.
What are the challenges to implementing AI? Tim Miller, Vice President of Life Sciences Platform Solutions at Elsevier, addresses these questions in the World Pharma Today article Why AI in Drug Discovery Has Yet to Live Up to Its Promise.
As a professional with over 14 years of experience in strategy development and partnership management across a variety of industries, Nickiâs latest role as a Senior Manager, Segment Marketing at Elsevier applies her skills to the area of drug discovery and development in the Pharma and Biotech industry.
In this capacity she is focused on understanding biopharmaceutical R&D challenges and turning them into opportunity to further Elsevierâs ability to serve industry executives and the professionals who innovate in the drug discovery and development space.
Nicki resides in New York City and holds a BA in English Literature and Mandarin Chinese from Carleton College in Northfield, MN.