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

Russian Chemical Bulletin

Volumn 52, Issue 1, 2003, Pages 20-29

  Artemenko, N V ^a   Baskin, I I ^a   Palyulin, V A ^a   Zefirov, N S ^a  

^a MOSCOW STATE UNIVERSITY   (Russian Federation)

Author keywords

Artificial neural networks; Density; Fragmental descriptors; Neural network modeling; Physicochemical properties; Vapor pressure; Viscosity

Indexed keywords

ALCOHOL DERIVATIVE; ALDEHYDE DERIVATIVE; ALKANE; ALKENE; ALKYNE; AMIDE; CARBOXYLIC ACID DERIVATIVE; ESTER DERIVATIVE; ETHER DERIVATIVE; HALOGEN; IMINE; KETONE DERIVATIVE; NITRILE; NITRO DERIVATIVE; ORGANIC COMPOUND; POLYCYCLIC AROMATIC HYDROCARBON; SULFUR DERIVATIVE;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CHEMICAL STRUCTURE; DATA BASE; DENSITY; ENTHALPY; LINEAR REGRESSION ANALYSIS; PHYSICAL CHEMISTRY; STATISTICAL ANALYSIS; STATISTICAL MODEL; VAPOR PRESSURE; VISCOSITY;

EID: 0037287399     PISSN: 10665285     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1022467508832     Document Type: Article

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