This session will look at how AI technology such as Deep Learning is being applied to the challenge of drug failure, both in discovery and in clinical trials. One of the primary drivers of R&D expense is the high number of failures that occur during the development of a therapeutic. Many pharmaceutical companies are now looking at AI solutions to better predict which assets will succeed in trials, and to design compounds have been optimized for success. Some specific examples:

• Using clinical trial outcomes to improve estimates of success.
• Currently approximately 50% of trials fail due to ADME/Tox issues – examples of how Pfizer is using
• DL to help change this.
• How is medicinal chemistry and design changing with AI?
• Using AI earlier in the discovery cycle to select targets and indications for investment.

This session features two case study presentations that will address the importance of data organization across an organization to unlock the value of the data being collected.
• Why one-size-fits-all systems don’t work (at least right now).
• Janssen’s “ecosystem of platforms” approach to taking existing systems for different purposes and integrating them for better data access
• Methods for capturing data sets to do analysis across the company instead of in silos.
• A view into current Janssen projects.