Pyrrole acetylenecarbaldehydes: an entry to a novel class of functionalized pyrroles

Gotsko, Maxim D.; Собенина, Л. Н.; Tomilin, Denis N.; Маркова, М. В.; Ushakov, Igor А.; Vakul’skaya, T. I.; Khutsishvili, Spartak S.; Трофимов, Б. А.

doi:10.1016/j.tetlet.2016.09.079

“…It is suggested [64] that the reaction mechanism in this case represents the direct nucleophilic substitution of chlorine atom by the pyrrole moiety. This is supported by known data [66,67] that the reactions of chloroethynylphosphonates with nucleophiles including the neutral ones proceed mainly as a nucleophilic substitution of chlorine atom at the C sp carbon.…”

Section: With Pyrrole-2-carbaldehydesmentioning

confidence: 99%

“…A series of substituted pyrroles 1 were ethynylated by iodopropiolaldehyde in solid K 2 CO 3 (a 10-fold excess) under mild conditions without solvent to afford highly reactive functionalized pyrrole compounds, 3-(pyrrol-2-yl)propiolaldehydes 2, in up to 40% yield (Scheme 1) [53]. The reagents were ground intensively for 5-10 min and allowed to stand at room temperature for 4 h. Iodopropioaldehyde was more preferable over explosive bromopropiolaldehyde.…”

Section: Cross-coupling Of Bromo-and Iodopropiolaldehydes With Pyrrolesmentioning

confidence: 99%

Recent Strides in the Transition Metal-Free Cross-Coupling of Haloacetylenes with Electron-Rich Heterocycles in Solid Media

Собенина

¹

,

Трофимов

²

2020

Self Cite

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The publications covering new, transition metal-free cross-coupling reactions of pyrroles with electrophilic haloacetylenes in solid medium of metal oxides and salts to regioselectively afford 2-ethynylpyrroles are discussed. The reactions proceed at room temperature without catalyst and base under solvent-free conditions. These ethynylation reactions seem to be particularly important, since the common Sonogashira coupling does not allow ethynylpyrroles with strong electron-withdrawing substituents at the acetylenic fragments to be synthesized. The results on the behavior of furans, thiophenes, and pyrazoles under the conditions of these reactions are also provided. The reactivity and structural peculiarities of nucleophilic addition to the activated acetylene moiety of the novel C-ethynylpyrroles are considered.

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“…So manual test is feasible, but for a limited number of samples. We took ten articles [34][35][36][37][38][39][40][41][42][43] to test our method on the real data. Structures that represent reaction mechanisms were excluded, so we regarded only molecules and Markush templates.…”

Section: Validation On Real Datamentioning

confidence: 99%

Image2SMILES: Transformer‐Based Molecular Optical Recognition Engine**

Khokhlov

¹

,

Krasnov

²

,

Fedorov

³

et al. 2022

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The rise of deep learning in various scientific and technology areas promotes the development of AI-based tools for information retrieval. Optical recognition of organic structures is a key part of the automated extraction of chemical information. However, this is a challenging task because there is a large variety of representation styles. In this research, we present a Transformer-based artificial neural network to convert images of organic structures to molecular structures. To train the model, we created a comprehensive data generator that stochastically simulates various drawing styles, functional groups, functional group placeholders (R-groups), and visual contamination. We demonstrate that the Transformer-based architecture can gather chemical insights from our generator with almost absolute confidence. That means that, with Transformer, one can fully concentrate on data simulation to build a good recognition model. A web demo of our optical recognition engine is available online at Syntelly platform, and the code for dataset generation is available on GitHub.

show abstract

“…So manual test is feasible, but for a limited number of samples. We took ten articles [28][29][30][31][32][33][34][35][36][37] to test our method on the real data. Structures that represent reaction mechanisms were excluded, so we regarded only molecules and Markush templates.…”

Section: Validation On Real Datamentioning

confidence: 99%

Image2SMILES: Transformer-based Molecular Optical Recognition Engine

Khokhlov¹,

Krasnov²,

Fedorov³

et al. 2021

Preprint

View full text Add to dashboard Cite

The rise of deep learning in various scientific and technology areas promotes the development of AI-based tools for information retrieval. Optical recognition of organic structures is a key part of the automated extraction of chemical information. However, this is a challenging task because there is a large variety of representation styles. In this research, we present a Transformer-based artificial neural network to convert images of organic structures to molecular structures. To train the model, we created a comprehensive data generator that stochastically simulates various drawing styles, functional groups, functional group placeholders (R-groups), and visual contamination. We demonstrate that the Transformer-based architecture can gather chemical insights from our generator with almost absolute confidence. That means that, with Transformer, one can fully concentrate on data simulation to build a good recognition model. A web demo of our optical recognition engine is available online at <i>Syntelly</i> platform.

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Pyrrole acetylenecarbaldehydes: an entry to a novel class of functionalized pyrroles

Cited by 6 publications

References 35 publications

Recent Strides in the Transition Metal-Free Cross-Coupling of Haloacetylenes with Electron-Rich Heterocycles in Solid Media

Recent Strides in the Transition Metal-Free Cross-Coupling of Haloacetylenes with Electron-Rich Heterocycles in Solid Media

Image2SMILES: Transformer‐Based Molecular Optical Recognition Engine**

Image2SMILES: Transformer-based Molecular Optical Recognition Engine

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