CAS at Pistoia Alliance Virtual Conference

Unique Insights for Faster Breakthroughs

Join us at the Pistoia Alliance Virtual Conference Week

October 19-23, 2020

What a defining year 2020 has become!  Collaboration has become even more essential to accelerating innovation.  Join us at Pistoia for executive panels, keynotes, and networking.

Looking to optimize your innovation outcomes for tomorrow’s therapeutics? We can help. Contact us to learn more.


Featured CAS Events and Presentations

R&D Leader Roundtable: Enabling Collaborative Research – Learnings from COVID

October 21, 2020 at 15:00 BST (10:00 EST)

What has Covid taught you about collaboration?  See what the industry leaders have learned.

A roundtable of Industry leaders led by Pistoia President Steve Arlington.
 

Panelists

  • Steve Arlington, (Chair) President, Pistoia Alliance
  • Thomas Hudson, SVP, R&D and Chief Scientific Officer, AbbVie
  • Bryn Roberts, SVP, Global Head of Operations for Pharma Research & Early Development, Roche
  • Merdad Parsey, EVP & CMO, Gilead
  • Manuel Guzman, President, CAS

Describing Chemistry to Algorithms: Why Scientific Expertise Improves AI Accuracy

October 21, 2020 at 16:30 BST (11:30 EST)

Abstract: More and better data is an obvious need, but have you considered how impactful descriptors can be on predictions? Join us to see how better descriptors can improve AI algorithm performance on predicting biological activity across multiple algorithmic approaches with over 150K compounds. This case study by CAS and Dr. Alpha Lee (University of Cambridge) was recently published in the Journal of Chemical Information and Modeling, and showcases the impact of better descriptors on prediction accuracy. If your AI initiatives aren’t meeting expectations, learn how better quality descriptors can immediately improve accuracy vs. existing fingerprints. 

Dr. Yugal Sharma

photo of Yugal Sharma, Senior Director, CAS
Senior Director, CAS

 

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Impact of Data Quality on Machine Learning Results

See how enhancing the quality of the input data alone, without changing the algorithm, impacts prediction accuracy of an algorithm designed to assess the biological activity of compounds relative to different targets in this quantitative study by CAS data scientists.

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