*This webinar will be held in English.

Date
July 7, 2022 

Time
14:00 - 15:20

Description

Waller and Segler et al. (Nature 2018, 555, 604–610) in collaboration with the Reaxys® team have developed a deep learning computer algorithm that produces blueprints for the sequences of reactions needed to create small organic molecules, such as drug-like compounds or natural products. Now, an artificial intelligence tool has been trained on more than 15 million unique single-step organic reactions from Reaxys®, which generates a retrosynthesis tree back to commercially available starting materials for a given target molecule. This approach has the potential to significantly streamline the way synthetic chemists approach retrosynthetic analysis in the future.

Herein, in today’s webinar we will present a case study involving drug-like compounds and natural products, analyzing the synthetic strategies that Reaxys® Predictive Retrosynthesis applies towards these molecules. We will evaluate the strategies proposed by Reaxys® Predictive Retrosynthesis in comparison to previously used synthetic strategies in our research group. We will discuss the current capabilities, future development, as well as limitations of the software on selected examples.

The webinar will cover in detail:
→ Introduction of the Reaxys Predictive Retrosynthesis tool 
→ Using a Chemists perspective, compare the advantages and disadvantages between manual synthesis planning and Reaxys AI based prediction paths. Results using various molecules are shared.Reaxys
→ Introduction of the trial of the Reaxys Predictive Retrosynthesis tool

Webinar Platform
Zoom 

Host
Elsevier

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