Inventing new drugs is incredibly difficult: on average, each project requires 5 years and over 4,000 synthesised compounds to produce a viable lead candidate. While many new techniques and technologies have been added to the biopharma arsenal, the hurdles and requirements have grown faster. The resulting continual decline in R&D productivity – even excluding the impacts of the COVID-19 pandemic – means that new approaches are required just to keep up.
As a result, many organisations have looked at AI and data science to maximise the value of their data. New methods as diverse as employing real world evidence in target identification and machine learning-driven drug design offer significant promise in jumpstarting R&D productivity. Teams are finding that successful initiatives are based on fundamentals, not fads. But what are these “fundamentals” and how can researchers best utilise them? Hear from experts from Novartis, GSK, AZ, Collaborations Pharmaceuticals and Dassault Systemes discuss the latest trends and best practices for AI and data science in drug discovery.
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