Chemoenzymatic synthesis of macrocyclic peptides and polyketides via thioesterase-catalyzed macrocyclization

• Senze Qiao,
• Zhongyu Cheng and
• Fuzhuo Li

Beilstein J. Org. Chem. 2024, 20, 721–733, doi:10.3762/bjoc.20.66

• stability, etc. Emerging research methods on bioinformatics, computational modeling, deep learning, protein engineering, and high-throughput screening will accelerate the pace of enzyme discovery to provide a broader platform of tools for employing chemoenzymatic strategies [64][87][88][89]. More

GlAIcomics: a deep neural network classifier for spectroscopy-augmented mass spectrometric glycans data

• Thomas Barillot,
• Baptiste Schindler,
• Baptiste Moge,
• Elisa Fadda,
• Franck Lépine and
• Isabelle Compagnon

Beilstein J. Org. Chem. 2023, 19, 1825–1831, doi:10.3762/bjoc.19.134

• intelligence in combination with spectroscopy-augmented mass spectrometry for carbohydrates sequencing and glycomics applications. Keywords: Bayesian neural network; deep learning; glycomics; IR; spectroscopy; Introduction DNA and protein sequencing technologies that aim at determining the structure of a

Navigating and expanding the roadmap of natural product genome mining tools

• Friederike Biermann,
• Sebastian L. Wenski and
• Eric J. N. Helfrich

Beilstein J. Org. Chem. 2022, 18, 1656–1671, doi:10.3762/bjoc.18.178

• -like structures and prioritized based on the taxonomic distribution of the cluster. decRiPPter was successfully used for the identification of a new lanthipeptide subfamily, providing experimental validation of the algorithm [65]. A more advanced form of supervised learning is deep learning (Figure 4

• ). An example of a deep learning architecture is the artificial neural network inspired by the human brain architecture. It consists of artificial neurons processing information organized in different layers and connected by synapses [73]. These advanced algorithms often provide higher accuracy in their

On drug discovery against infectious diseases and academic medicinal chemistry contributions

• Yves L. Janin

Beilstein J. Org. Chem. 2022, 18, 1355–1378, doi:10.3762/bjoc.18.141

• issue in this regard as a major portion of published data will have to be filtered out before such methods starts to make some tangible headways [57]. For instance, a recent “deep-learning” search for new antibiotics came out with the finding that halicin (1) depicted in Figure 1 was, as many nitro

Models of necessity

• Timothy Clark and
• Martin G. Hicks

Beilstein J. Org. Chem. 2020, 16, 1649–1661, doi:10.3762/bjoc.16.137

• changed recently is the amount of data that is available and the upsurge of deep-learning algorithms, which date back to the late 1960’s [65] but were preceded in chemistry by far simpler back-propagation neural nets [66] and made their first impact around 2015 [67]. At the second Beilstein Bozen

Biomimetic molecular design tools that learn, evolve, and adapt

• David A Winkler

Beilstein J. Org. Chem. 2017, 13, 1288–1302, doi:10.3762/bjoc.13.125

• methods and their potential impacts in chemistry, engineering, and medicine. Keywords: automated chemical synthesis; deep learning; evolutionary algorithms; in silico evolution; machine learning; materials design and development; neural networks; Introduction There is still not a clear understanding of

• future impact. It introduces the most common type of algorithm, machine learning. A discussion of a very useful machine-learning algorithm, the neural network follows, and problems that often arise in their use, and solutions to these difficulties described. A new type of deep learning neural network

• paradigm shifting new variants called deep learning. We provide a brief summary of these types of machine learning algorithms to assist those organic chemists who are not familiar with them. Traditional backpropagation algorithm A common machine learning algorithm is the backpropagation neural network