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A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target. | LitMetric

A genetic optimization strategy with generality in asymmetric organocatalysis as a primary target.

Chem Sci

Laboratory for Computational Molecular Design, Institute of Chemical Sciences and Engineering, Ecole Polytechnique Fédérale de Lausanne (EPFL) 1015 Lausanne Switzerland

Published: March 2024

AI Article Synopsis

  • A "general" catalyst can work well with many different substrates and performs efficiently in asymmetric synthesis, but discovering truly general catalysts is challenging and costly.
  • Existing computational methods help assess known catalysts but can’t create new ones with better performance, prompting the need for innovative strategies.
  • The study introduces an inverse design approach using the NaviCatGA genetic algorithm and OSCAR database to explore and optimize a wide range of catalysts, specifically applied to the Pictet-Spengler condensation, ultimately revealing valuable insights into catalyst performance across diverse chemical scenarios.

Article Abstract

A catalyst possessing a broad substrate scope, in terms of both turnover and enantioselectivity, is sometimes called "general". Despite their great utility in asymmetric synthesis, truly general catalysts are difficult or expensive to discover traditional high-throughput screening and are, therefore, rare. Existing computational tools accelerate the evaluation of reaction conditions from a pre-defined set of experiments to identify the most general ones, but cannot generate entirely new catalysts with enhanced substrate breadth. For these reasons, we report an inverse design strategy based on the open-source genetic algorithm NaviCatGA and on the OSCAR database of organocatalysts to simultaneously probe the catalyst and substrate scope and optimize generality as a primary target. We apply this strategy to the Pictet-Spengler condensation, for which we curate a database of 820 reactions, used to train statistical models of selectivity and activity. Starting from OSCAR, we define a combinatorial space of millions of catalyst possibilities, and perform evolutionary experiments on a diverse substrate scope that is representative of the whole chemical space of tetrahydro-β-carboline products. While privileged catalysts emerge, we show how genetic optimization can address the broader question of generality in asymmetric synthesis, extracting structure-performance relationships from the challenging areas of chemical space.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10915838PMC
http://dx.doi.org/10.1039/d3sc06208bDOI Listing

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