Machine learning-guided strategies for reaction conditions design and optimization

• Lung-Yi Chen and
• Yi-Pei Li

Beilstein J. Org. Chem. 2024, 20, 2476–2492, doi:10.3762/bjoc.20.212

• efficiency and effectiveness of reaction conditions design, and enable novel discoveries in synthetic chemistry. Keywords: data preprocessing; reaction conditions prediction; reaction data mining; reaction optimization; reaction representation; Introduction Machine learning (ML) techniques have been widely

• aspects of the dataset features and data preprocessing methods. Moreover, we introduce common algorithms and representative studies for developing both global and local models. We highlight representative studies that demonstrate the effectiveness and applicability of these algorithms in real-world

• capture of experimental records of chemical reactions from chemical synthesis papers. This development demonstrates significant advancements in streamlining the data extraction process for chemical research. Implicit data issues and data preprocessing tools The quality of training data is a crucial factor

Semiautomated glycoproteomics data analysis workflow for maximized glycopeptide identification and reliable quantification

• Steffen Lippold,
• Arnoud H. de Ru,
• Jan Nouta,
• Peter A. van Veelen,
• Magnus Palmblad,
• Manfred Wuhrer and
• Noortje de Haan

Beilstein J. Org. Chem. 2020, 16, 3038–3051, doi:10.3762/bjoc.16.253

• using msconvert (ProteoWizard 3.0 suite). The data preprocessing included the deconvolution of all MS1 signals using an OpenMS workflow (KNIME_OPENMS_GraphMS_Preprocessing_120318) in KNIME [15][33][34]. This workflow was used with OpenMS 2.3. Adaptions in the parameters were made in the m/z range of 400

Secondary metabolites of Bacillus subtilis impact the assembly of soil-derived semisynthetic bacterial communities

• Heiko T. Kiesewalter,
• Carlos N. Lozano-Andrade,
• Mikael L. Strube and
• Ákos T. Kovács

Beilstein J. Org. Chem. 2020, 16, 2983–2998, doi:10.3762/bjoc.16.248

• -throughput sequencing on an Illumina NovaSeq 6000 platform with 2 million reads (2 × 250 bp paired-end reads). Raw sequence data is available at NCBI: PRJNA658074. Sequencing data preprocessing The multiplexed sequencing data was imported into the QIIME 2 pipeline (version 2020.6) [65][66]. The paired-end