Total Synthesis of Haliclonin A

Jin, Yuan; Orihara, Kensuke; Kawagishi, Fumiki; Toma, Tatsuya; Fukuyama, Tohru; Yokoshima, Satoshi

doi:10.1002/anie.202016343

Cited by 5 publications

(3 citation statements)

References 48 publications

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“…The data sets used in this study are shown in Additional file 1: Table S2. Finally, to further verify the performance of DeepSA for compounds with real synthetic pathways, we selected 18 compounds with complete synthetic pathways from the published literatures [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48], which will be detailed introduced in the section of Results and Discussion.…”

Section: Collection Of Datasetsmentioning

confidence: 99%

DeepSA: a deep-learning driven predictor of compound synthesis accessibility

Wang,

et al. 2023

J Cheminform

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With the continuous development of artificial intelligence technology, more and more computational models for generating new molecules are being developed. However, we are often confronted with the question of whether these compounds are easy or difficult to synthesize, which refers to synthetic accessibility of compounds. In this study, a deep learning based computational model called DeepSA, was proposed to predict the synthesis accessibility of compounds, which provides a useful tool to choose molecules. DeepSA is a chemical language model that was developed by training on a dataset of 3,593,053 molecules using various natural language processing (NLP) algorithms, offering advantages over state-of-the-art methods and having a much higher area under the receiver operating characteristic curve (AUROC), i.e., 89.6%, in discriminating those molecules that are difficult to synthesize. This helps users select less expensive molecules for synthesis, reducing the time and cost required for drug discovery and development. Interestingly, a comparison of DeepSA with a Graph Attention-based method shows that using SMILES alone can also efficiently visualize and extract compound’s informative features. DeepSA is available online on the below web server (https://bailab.siais.shanghaitech.edu.cn/services/deepsa/) of our group, and the code is available at https://github.com/Shihang-Wang-58/DeepSA.

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Section: Collection Of Datasetsmentioning

confidence: 99%

DeepSA: a deep-learning driven predictor of compound synthesis accessibility

Wang,

et al. 2023

J Cheminform

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“…ZnX 2 , 25%; 2. NaBH 4 , 40%) to produce a mixture of regioisomers of olefinic double bonds, represented a notable and pioneering achievement for the biomimetic synthesis of the bicyclic core of halicyclamines. , In comparison to previous biomimetic approaches, ,− ,, the operationally simple, metal-free, and atom-economical dimerization developed herein allowed high-yielding and rapid access to an intricate dimeric scaffold bearing higher oxidation levels, which could offer a versatile platform for further synthetic manipulations leading to natural products. , …”

Section: Resultsmentioning

confidence: 99%

Regiocontrolled Dimerization of Densely Functionalized 1,6-Dihydropyridines for the Biomimetic Synthesis of a Halicyclamine-type Scaffold by Preventing Disproportionation

2022

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The biomimetic dimerization of 1,6-dihydropyridines (DHPs) remains a daunting challenge due to competitive disproportionation pathways. Herein we report the regioselective dimerization of densely functionalized 1,6-DHPs that allow direct access to the bis-nitrogen bicyclic scaffold of halicyclamines. Disproportionation triggered by the hydride shift of 1,6-DHP was suppressed by the use of geminal disubstituted substrates. Installation of an electron-withdrawing substituent at the C3 position was demonstrated to be crucial for facilitating biomimetic dimerization under metal-free conditions, with exquisite control of regioselectivity at ambient temperature. Our approach, featuring an appropriately functionalized and substantially stabilized substrate rather than merely adopting the highly reactive and labile hypothetical biosynthetic intermediate, allowed gram-scale and atom-economical synthesis of the bis-nitrogen bicyclic scaffold. Furthermore, conversion of a series of 1,6-DHPs provided mechanistic insights by circumventing the competitive disproportionation reaction. This revealed not only the innate reactivity of the conjugate diene system for [4 + 2] cycloaddition but also the reversibility of the dimerization reaction with multiple cationic intermediates in equilibrium.

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“…Precisely forging unsaturated molecular frameworks with the desired regio- and stereochemistry has always been a significant task and considerable challenge in organic synthesis. In this context, the metal-catalyzed hydroallylation of alkynes with allylic electrophiles or nucleophiles to construct skipped dienes continues to attract great attention, as the skipped diene scaffolds are valuable structural motifs in many bioactive natural products, 1 and are also specific building blocks in organic synthesis. 2 However, their development remains relatively sluggish compared with the well-established Alder-ene reactions, 3 due to the difficulty of controlling the regio- and stereoselectivity.…”

Section: Introductionmentioning

confidence: 99%