October 26-27 2020 Boston, MA
The Event Overview for the AI Applications Summit is now available to download!
1. The passionate discussion and debate during the two days of the Summit made it clear that the adoption of AI tools is continuing at a fast pace and everyone attending is laser focused on driving progress to provide critical treatments to patients faster.
2. Talent management continues to be a key issue and speakers shared their thoughts about how to tackle today’s challenges in hiring and retaining critical staff. One speaker summed up the general consensus that, “It’s not about the sheer number of data scientists, it’s about the right people in the right place and incentivized in the right way.” The group also agreed that pharma has a noble mission that people are attracted to and this needs to be emphasized more to compete for talent effectively.
3. Data management and RWD were top of mind for many attendees. Valuable information was presented by the FDA providing an update on FDA’s real-world evidence program. The topics of data access, data bias and data privacy are critical to get right as policy decisions are being put in place that will impact biopharma companies as well as patients.
4. Deep dive sessions discussed how AI is in fact delivering value today but that new business models will become a necessary path to survival and evolution. Key learnings from these sessions include:
· Focus on building the right thing, not just building things right;
· Prioritize projects using complexity vs. value to find “low hanging fruit”; and
· Execute POC/POV projects lean startup style and then use the learnings to scale up.
5. All agreed that getting AI right will result in making a remarkable impact that starts with data.
Currently, drug discovery is a blind search through chemical libraries, which is not that efficient. The AI Applications Summit is an important gathering of thought leaders to discuss better options, such as generative AI models, which can potentially speed up the drug discovery process by proposing a limited amount of high promising molecules instead of thousands with unknown activities and possibly increase the drug space (finding new drug, a new mechanism of action, new chemistry).
Polina Mamoshina, Research Scientist,Pharma AI a division of Insilico Medicine