Natural Plant Chemistry Search Strategies in CAS SciFinder (Presented in Chinese)

CAS SciFindern Webinar

CAS SciFindern, the most advanced science-aware relevance engine in the industry, leverages the largest curated source of scientific information in the world. The most comprehensive source from substances, chemical structures, and reactions to prior art search. Supporting early stage exploration from retrosynthetic planning to biosequence searching, CAS SciFindern accelerates breakthrough innovation by making unseen connections using hindsight, insight and foresight. 

This Chinese language webinar will focus on search strategies for natural product isolation and characterization, structure-activity relationships, and total synthesis and modification of natural products.


October 11, 2022 from 18:00-19:00 Beijing Time

*Content and features/functionality may vary based upon product license.


Written By

Dr. Cyrene Qian

Senior Customer Success Specialist/ACSI China

Dr. Cyrene Qian joined CAS in November 2015. She is responsible for the training, promotion and designing the case study for CAS solutions. Dr. Qian is an expert in retrieving scientific information via CAS SciFinder Discovery Platform and other CAS solutions; and she is also familiar with different types of chemical information searching tools. Before joining CAS, Dr. Qian worked as Associated Professor at the National Center for NANO Science and Technology, Chinese Academy of Sciences. Before that she did her post-doc research in the Dept. of Materials, University of Oxford since October 2013. She received her PhD degree in organic chemistry in Ecole Polytechnique, France; she got her master’s degree in chemistry in Memorial University, Canada and she got her bachelor’s degree of Materials Chemistry and bachelor’s degree of Business in Nankai University.
Date & Time

Tuesday, October 11, 2022

6:00am – 7:00am EDT



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Friday, November 24, 2023 · 1:00 – 2:00 am EST | Online

The below content will be introduced during the webinar.
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Synthesis strategies of drug molecules and optimization of AI-driven synthesis methods.