Prediction of Major Regio-, Site-, and Diastereoisomers in Diels-Alder Reactions by Using Machine-Learning: The Importance of Physically Meaningful Descriptors

被引：102

作者：

Beker, Wiktor ^{[1
]}

Gajewska, Ewa P. ^{[1
]}

Badowski, Tomasz ^{[1
]}

Grzybowski, Bartosz A. ^{[1
,2
,3
]}

机构：

[1] Polish Acad Sci, Inst Organ Chem, Ul Kasprzaka 44-52, PL-01224 Warsaw, Poland

[2] UNIST, Ctr Soft & Living Matter, 50 UNIST Gil, Ulsan, South Korea

[3] UNIST, Dept Chem, 50 UNIST Gil, Ulsan, South Korea

来源：

ANGEWANDTE CHEMIE-INTERNATIONAL EDITION | 2019年 / 58卷 / 14期

关键词：

Diels-Alder reaction; machine learning; neural networks; Random Forest; selectivity; NEURAL-NETWORKS; CHEMICAL-REACTIONS; ORGANIC-CHEMISTRY; REACTIVITY; REGIOSELECTIVITY;

D O I：

10.1002/anie.201806920

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

Machine learning can predict the major regio-, site-, and diastereoselective outcomes of Diels-Alder reactions better than standard quantum-mechanical methods and with accuracies exceeding 90% provided that i) the diene/dienophile substrates are represented by physical-organic descriptors reflecting the electronic and steric characteristics of their substituents and ii) the positions of such substituents relative to the reaction core are encoded (vectorized) in an informative way.

引用

页码：4515 / 4519

页数：5

共 54 条

[31] Mullin R., 2017, Chem. Eng. News, V95, P26
[32] Nicolaou K.C., 2002, Angew. Chem, V114, P1742, DOI DOI 10.1002/1521-3757(20020517)114:10<1742::AID-ANGE1742>3.0.CO
[33] 2-Y
[34] Nicolaou K.C., 2001, Angew. Chem. Int. Ed, V113, P2543
[35] Nicolaou KC, 2002, ANGEW CHEM INT EDIT, V41, P1668, DOI 10.1002/1521-3773(20020517)41:10<1668::AID-ANIE1668>3.0.CO
[36] 2-Z
[37] Nicolaou KC, 2001, ANGEW CHEM INT EDIT, V40, P2482, DOI 10.1002/1521-3773(20010702)40:13<2482::AID-ANIE2482>3.0.CO
[38] 2-A
[39] Learning from the Harvard Clean Energy Project: The Use of Neural Networks to Accelerate Materials Discovery
Pyzer-Knapp, Edward O.
Li, Kewei
Aspuru-Guzik, Alan
[J]. ADVANCED FUNCTIONAL MATERIALS, 2015, 25 (41) : 6495 - 6502
[40] Machine-learning-assisted materials discovery using failed experiments
Raccuglia, Paul
Elbert, Katherine C.
Adler, Philip D. F.
Falk, Casey
Wenny, Malia B.
Mollo, Aurelio
Zeller, Matthias
Friedler, Sorelle A.
Schrier, Joshua
Norquist, Alexander J.
[J]. NATURE, 2016, 533 (7601) : 73 - +

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