Scheduled for October 7, 2020, 11 am to 12:00 pm EDT
While AI has already begun to transform healthcare in numerous ways, when it comes to applying AI to early-stage drug discovery for critical diseases, progress has been much slower. While drug discovery successes have been achieved, there have been just as many high-profile setbacks. Given what the industry has learned and experienced already, what are the main barriers to success and what is realistic to expect from AI in the near future?
This panel will explore the role of data and molecular descriptors in optimizing predictive models in the biopharma R&D pipeline. As vital as they are, models are only as good as the data that drives them. We will look at recent case studies to identify key considerations for maximizing the ROI of your drug discovery AI investment. Specifically, we’ll address:
- How data and descriptors influence model success
- Overcoming common challenges of data quality for better model accuracy
- Improving predictions of biological activity to prioritize most-likely candidates
- Identifying target molecules and potential therapeutics with less time in the lab
Dr. Yugal Sharma, Senior Director, CAS Services
Dr. Yugal Sharma, currently the Senior Director for CAS Services, has over 15 years of experience in applying and managing data science approaches to solve complex problems in healthcare. Prior to joining CAS, he spent time at the National Institutes of Health (NIH), focusing on developing early disease detection algorithms. Yugal also helped found a technology startup and a business consulting startup. Most recently, he applied his background as a consultant focusing on analytics strategy for clients in the federal and commercial space. He has published several scientific articles, as well co-authoring a book chapter on the mining of Electronic Health Records to detect disease signals. Yugal received his PhD in Biophysics from University of Cincinnati, where he graduated with honors.
