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Finding Relevant Retrosynthetic Disconnections for Stereocontrolled Reactions.
Wiest, Olaf; Bauer, Christoph; Helquist, Paul; Norrby, Per-Ola; Genheden, Samuel.
Afiliação
  • Wiest O; Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States.
  • Bauer C; Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, Sweden.
  • Helquist P; Department of Chemistry and Biochemistry, University of Notre Dame, Notre Dame, Indiana 46556, United States.
  • Norrby PO; Data Science and Modelling, Pharmaceutical Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, Sweden.
  • Genheden S; Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Pepparedsleden 1, SE-431 83 Mölndal, Sweden.
J Chem Inf Model ; 64(15): 5796-5805, 2024 Aug 12.
Article em En | MEDLINE | ID: mdl-38995078
ABSTRACT
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.
Assuntos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Aprendizado de Máquina Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Aprendizado de Máquina Idioma: En Revista: J Chem Inf Model Assunto da revista: INFORMATICA MEDICA / QUIMICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Estados Unidos