Artificial neural network and fragmental approach in prediction of physicochemical properties of organic compounds

被引：20

作者：

Artemenko, NV ^{[1
]}

Baskin, II ^{[1
]}

Palyulin, VA ^{[1
]}

Zefirov, NS ^{[1
]}

机构：

[1] Moscow MV Lomonosov State Univ, Dept Chem, Moscow 119992, Russia

来源：

RUSSIAN CHEMICAL BULLETIN | 2003年 / 52卷 / 01期

关键词：

artificial neural networks; neural network modeling; viscosity; density; vapor pressure; physicochemical properties; fragmental descriptors; LIQUID VISCOSITY; VAPOR-PRESSURE; PHYSICAL-PROPERTIES; BIOLOGICAL-ACTIVITY; QSPR PREDICTION; DESCRIPTORS; HYDROCARBONS; MODEL;

D O I：

10.1023/A:1022467508832

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

An approach based on fragmental descriptors (occurrence number of structural fragments in chemical structures) in conjunction with the artificial neural network technique was developed for predicting the physicochemical properties of organic compounds. The construction of neural network models for predicting the viscosity, density, and saturated vapor pressure for various classes of organic compounds is discussed.

引用

页码：20 / 29

页数：10

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