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Finding Relevant Retrosynthetic Disconnections for Stereocontrolled Reactions.

Olaf G WiestChristoph Alexander BauerPaul HelquistPer-Ola NorrbySamuel Genheden
Published in: Journal of chemical information and modeling (2024)
Machine learning-driven computer-aided synthesis planning (CASP) tools have become important tools for idea generation in the design of complex molecule synthesis but do not adequately address the stereochemical features of the target compounds. A novel approach to automated extraction of templates used in CASP that includes stereochemical information included in the US Patent and Trademark Office (USPTO) and an internal AstraZeneca database containing reactions from Reaxys, Pistachio, and AstraZeneca electronic lab notebooks is implemented in the freely available AiZynthFinder software. Three hundred sixty-seven templates covering reagent- and substrate-controlled as well as stereospecific reactions were extracted from the USPTO, while 20,724 templates were from the AstraZeneca database. The performance of these templates in multistep CASP is evaluated for 936 targets from the ChEMBL database and an in-house selection of 791 AZ designs. The potential and limitations are discussed for four case studies from ChEMBL and examples of FDA-approved drugs.
Keyphrases
  • machine learning
  • adverse drug
  • deep learning
  • high throughput
  • emergency department
  • risk assessment
  • health information
  • single cell
  • drug induced
  • structural basis