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量子化学计算的[3+2]环加成反应的反应剖面。

Reaction profiles for quantum chemistry-computed [3 + 2] cycloaddition reactions.

机构信息

Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts, 02139, USA.

Department of Computer Science, University of Toronto, 40 St George St, Toronto, Ontario, M5S 2E4, Canada.

出版信息

Sci Data. 2023 Feb 1;10(1):66. doi: 10.1038/s41597-023-01977-8.

DOI:10.1038/s41597-023-01977-8
PMID:36725850
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9892576/
Abstract

Bio-orthogonal click chemistry based on [3 + 2] dipolar cycloadditions has had a profound impact on the field of biochemistry and significant effort has been devoted to identify promising new candidate reactions for this purpose. To gauge whether a prospective reaction could be a suitable bio-orthogonal click reaction, information about both on- and off-target activation and reaction energies is highly valuable. Here, we use an automated workflow, based on the autodE program, to compute over 5000 reaction profiles for [3 + 2] cycloadditions involving both synthetic dipolarophiles and a set of biologically-inspired structural motifs. Based on a succinct benchmarking study, the B3LYP-D3(BJ)/def2-TZVP//B3LYP-D3(BJ)/def2-SVP level of theory was selected for the DFT calculations, and standard conditions and an (aqueous) SMD model were imposed to mimic physiological conditions. We believe that this data, as well as the presented workflow for high-throughput reaction profile computation, will be useful to screen for new bio-orthogonal reactions, as well as for the development of novel machine learning models for the prediction of chemical reactivity more broadly.

摘要

基于[3+2]偶极环加成的生物正交点击化学对生物化学领域产生了深远的影响,人们为此投入了大量的精力来确定有前途的新候选反应。为了评估一个预期的反应是否可以成为一种合适的生物正交点击反应,关于靶上和靶外激活以及反应能的信息是非常有价值的。在这里,我们使用基于 autodE 程序的自动化工作流程,计算了涉及合成双烯和亲偶极体以及一组生物启发结构基序的[3+2]环加成的 5000 多个反应剖面。基于简洁的基准研究,选择 B3LYP-D3(BJ)/def2-TZVP//B3LYP-D3(BJ)/def2-SVP 理论水平进行 DFT 计算,并施加标准条件和(水相)SMD 模型以模拟生理条件。我们相信,这些数据以及用于高通量反应剖面计算的呈现工作流程,将有助于筛选新的生物正交反应,以及更广泛地开发用于预测化学反应性的新型机器学习模型。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/f9023a688322/41597_2023_1977_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/ea5e0538cc9d/41597_2023_1977_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/058537779efe/41597_2023_1977_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/db184e2788a0/41597_2023_1977_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/5949fb2cc818/41597_2023_1977_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/de3a91963baf/41597_2023_1977_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/95ad8800c6f0/41597_2023_1977_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/715c5b5305e2/41597_2023_1977_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/f9023a688322/41597_2023_1977_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/ea5e0538cc9d/41597_2023_1977_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/6acbdbb3eb96/41597_2023_1977_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/287981523658/41597_2023_1977_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/058537779efe/41597_2023_1977_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/db184e2788a0/41597_2023_1977_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/5949fb2cc818/41597_2023_1977_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/de3a91963baf/41597_2023_1977_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/95ad8800c6f0/41597_2023_1977_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/715c5b5305e2/41597_2023_1977_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae58/9892576/f9023a688322/41597_2023_1977_Fig10_HTML.jpg

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