Calibration-free quantification and automated data analysis for high-throughput reaction screening

被引:1
|
作者
Katzenburg, Felix [1 ]
Boser, Florian [1 ]
Schaefer, Felix R. [1 ]
Pfluger, Philipp M. [1 ]
Glorius, Frank [1 ]
机构
[1] Westfal Wilhelms Univ, Organ Chem Inst, Corrensstrasse 40, D-48149 Munster, Germany
来源
DIGITAL DISCOVERY | 2025年 / 4卷 / 02期
关键词
MASS-SPECTROMETRY; DISCOVERY; HYDROGENATION; OPTIMIZATION; PLATFORM; PHENOLS; TOOL;
D O I
10.1039/d4dd00347k
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The accelerated generation of reaction data through high-throughput experimentation and automation has the potential to boost organic synthesis. However, efforts to generate diverse reaction datasets or identify generally applicable reaction conditions are still hampered by limitations in reaction yield quantification. In this work, we present an automatable screening workflow that facilitates the analysis of reaction arrays with distinct products without relying on the isolation of product references for external calibrations. The workflow is enabled by a flexible liquid handler and parallel GC-MS and GC-Polyarc-FID analysis while we introduce pyGecko, an open-source Python library for processing GC raw data. pyGecko offers comprehensive analysis tools allowing for the determination of reaction outcomes of a 96-reaction array in under a minute. Our workflow's utility is showcased for the scope evaluation of a site-selective thiolation of halogenated heteroarenes and the comparison of four cross-coupling protocols for challenging C-N bond formations.
引用
收藏
页码:384 / 392
页数:9
相关论文
共 50 条
  • [1] High-throughput screening of catalysts by combining reaction and analysis
    Trapp, Oliver
    Weber, Sven K.
    Bauch, Sabrina
    Hofstadt, Werner
    ANGEWANDTE CHEMIE-INTERNATIONAL EDITION, 2007, 46 (38) : 7307 - 7310
  • [2] Quantification of frequent-hitter behavior based on historical high-throughput screening data
    Nissink, J. Willem M.
    Blackburn, Sam
    FUTURE MEDICINAL CHEMISTRY, 2014, 6 (10) : 1113 - 1126
  • [3] An Automated Pipeline for High-Throughput Label-Free Quantitative Proteomics
    Weisser, Hendrik
    Nahnsen, Sven
    Grossmann, Jonas
    Nilse, Lars
    Quandt, Andreas
    Brauer, Hendrik
    Sturm, Marc
    Kenar, Erhan
    Kohlbacher, Oliver
    Aebersold, Ruedi
    Malinstroemt, Lars
    JOURNAL OF PROTEOME RESEARCH, 2013, 12 (04) : 1628 - 1644
  • [4] Quantitative high-throughput screening data analysis: challenges and recent advances
    Shockley, Keith R.
    DRUG DISCOVERY TODAY, 2015, 20 (03) : 296 - 300
  • [5] Automated High-Throughput System Combining Small-Scale Synthesis with Bioassays and Reaction Screening
    Morato, Nicolas M.
    Le, MyPhuong T.
    Holden, Dylan T.
    Cooks, R. Graham
    SLAS TECHNOLOGY, 2021, 26 (06): : 555 - 571
  • [6] Towards Exhaustive and Automated High-Throughput Screening for Crystalline Polymorphs
    Pfund, Laura Y.
    Matzger, Adam J.
    ACS COMBINATORIAL SCIENCE, 2014, 16 (07) : 309 - 313
  • [7] A Novel Automated Framework for QSAR Modeling of Highly Imbalanced Leishmania High-Throughput Screening Data
    Casanova-Alvarez, Omar
    Morales-Helguera, Aliuska
    Angel Cabrera-Perez, Miguel
    Molina-Ruiz, Reinaldo
    Molina, Christophe
    JOURNAL OF CHEMICAL INFORMATION AND MODELING, 2021, 61 (07) : 3213 - 3231
  • [8] High-throughput screening of Weyl semimetals
    Grassano, Davide
    Marzari, Nicola
    Campi, Davide
    PHYSICAL REVIEW MATERIALS, 2024, 8 (02)
  • [9] GUItars: A GUI Tool for Analysis of High-Throughput RNA Interference Screening Data
    Goktug, Asli N.
    Ong, Su Sien
    Chen, Taosheng
    PLOS ONE, 2012, 7 (11):
  • [10] High-Throughput Screening of Metabolic Biomarkers and Wearable Biosensors for the Quantification of Metabolites
    Zhang, Ru
    Qian, Kun
    ADVANCED SENSOR RESEARCH, 2023, 2 (03):