CAS Events at Medicinal Chemistry & Protein Degradation Summit

Unique Insights for Faster Breakthroughs

Join CAS at this exciting virtual event November 16th and 17th

The combined 4th Global Pharma R&D AI, Data Science and Informatics Summit & 5th Annual Medicinal Chemistry and Protein Degradation Summit

Looking to optimize your small molecules for tomorrow’s therapeutics?  We can help, contact us to learn more.


Featured CAS Events and Presentations

Exploring PROTAC Candidates and Related Immunoconjugates with SciFinderⁿ

Inspiring New Approaches in Protein Degradation Track:  Nov 16th at 10:20 am GMT 

Abstract: Pharmaceutical research teams increasingly rely on SciFindern, the most advanced research solution in the SciFinder family, to help them improve productivity, inspire innovation and feel confident in advancing their projects and programs forward.

This talk demonstrates, with practical examples, how SciFindern helps solve problems and inspire new ideas in protein degradation technology.


Mr. Jan Baur

Jan Baur, ACSI Application Specialist
CAS Application Specialist


Describing Chemistry to Algorithms: Why Scientific Expertise Improves AI Accuracy

Nov 17 at 10:10 a.m. GMT

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 Dr. Alpha Lee (University of Cambridge), which was just recently peer reviewed and accepted for publication in the Journal of Chemical Information and Modeling, 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



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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