Artificial intelligence in synthetic chemistry: achievements and prospects

被引：46

作者：

Baskin, Igor I. ^{[1
,2
,4
,5
,6
]}

Madzhidov, Timur I. ^{[2
,6
,7
]}

Antipin, Igor S. ^{[2
,7
,8
,9
]}

Varnek, Alexandre A. ^{[2
,3
,6
,10
]}

机构：

[1] Lomonosov Moscow State Univ, Fac Phys, Leninskie Gory 1,Stroenie 2, Moscow 119991, Russia

[2] Kazan Volga Redg Fed Univ, AM Butlerov Inst Chem, Ul Kremlevskaya 18, Kazan 420008, Russia

[3] Univ Strasbourg, CNRS, UniStra, Lab Chemoinformat,UMR 7140, 4 Rue Blaise Pascal, F-67000 Strasbourg, France

[4] MSU, Phys & Math Sci, Moscow, Russia

[5] MSU, Fac Phys, Moscow, Russia

[6] KFU, Kazan, Russia

[7] KFU, Chem Sci, Kazan, Russia

[8] RAS, Moscow, Russia

[9] KFU, Dept Organ Chem, Moscow, Russia

[10] Univ Strasbourg, Strasbourg, France

来源：

RUSSIAN CHEMICAL REVIEWS | 2017年 / 86卷 / 11期

基金：

俄罗斯科学基金会;

关键词：

STRUCTURE-REACTIVITY RELATIONSHIP; EXPERT SYSTEMS-APPROACH; COMPUTER-AIDED-DESIGN; GENOME-SCALE CLASSIFICATION; MOLECULAR-FIELD ANALYSIS; ORGANIC-REACTION SCHEMES; DATA MINING TECHNIQUES; REACTION-MATRIX TYPES; DE-NOVO DESIGN; NEURAL-NETWORK;

D O I：

10.1070/RCR4746

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

The review is devoted to the achievements in analysis of information on chemical reactions using machine learning methods. Four large areas that actively use these methods are outlined: computer-assisted planning of synthesis, analysis and visualization of chemical reaction data, prediction of the quantitative characteristics of reactions and computer-aided design of catalysts. The bibliography includes 346 references.

引用

页码：1127 / 1156

页数：30

共 343 条

[41] A new statistical approach to predicting aromatic hydroxylation sites. Comparison with model-based approaches [J].