Exploring ultrafast flow chemistry by autonomous self-optimizing platform

Gwang Noh Ahn, Ji Ho Kang, Hyune Jea Lee, Byung Eon Park, Minjun Kwon, Gi Su Na, Heejin Kim, Dong Hwa Seo, Dong Pyo Kim

Research output: Contribution to journalArticlepeer-review

Abstract

The rapid development of novel synthetic routes for pharmaceutical compounds is highly attractive for overcoming pandemic and epidemic-prone diseases like COVID-19. Herein, we report an automated microreactor platform (AMP) with Bayesian optimization (BO) that can autonomously explore the optimal conditions for ultrafast synthesis of biologically active thioquinazolinone. First, AMP operation is successfully demonstrated with full control of quantitative variables, specifically reaction volume, temperature, and flow rate, allowing to sequentially conduct a total of 80 experiments planned by the user. Next, BO enables the AMP to autonomously self-optimize the reaction conditions, demonstrating the high efficiency of the fully automated AMP. The fully automated approach is extended to optimize more complex variables including a categorical variable (i.e. the type of organolithium for synthesis), revealing that phenyllithium (PhLi) gives superior yield for synthesizing thioquinazolinone. In addition, the autonomous AMP is utilized for combinatorial chemistry to sequentially synthesize a library composed of nine types of S-benzylic thioquinazolinone under autonomously optimized conditions within only 20 min.

Original languageEnglish
Article number139707
JournalChemical Engineering Journal
Volume453
DOIs
Publication statusPublished - 2023 Feb 1

Keywords

  • Automated microreactor
  • Bayesian optimization
  • In-line analysis
  • Self-optimization
  • Ultrafast chemistry

ASJC Scopus subject areas

  • Chemistry(all)
  • Environmental Chemistry
  • Chemical Engineering(all)
  • Industrial and Manufacturing Engineering

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