title: [Computer-aided prediction of the mutagenic activity substituted polycyclic compounds].
abstract: The relationship between mutagenic activity and chemical structure was studied for 54 polycyclic compounds using two approaches: multiple linear regression analysis and artificial neural networks. Structural fragments, quantum chemical indices, and hydrophobicity (octanol-water partition coefficient) were used as descriptors (properties of the molecules introduced in the model). Both linear regression equations and nonlinear relationships obtained with the help of a neural network were shown to accurately predict mutagenic activity for the compounds structurally similar to those in the training sample. The introduction of experimentally selected descriptors is substantiated to verify the proposed mechanism of related compounds mutagenic activity.
authors.affiliation: Vavilov Institute of General Genetics, Russian Academy of Sciences, ul. Gubkina 3, Moscow, 117809 Russia.
authors.forename: I K
authors.initials: IK
authors.lastname: Liubimova
authors.forename: S K
authors.initials: SK
authors.lastname: Abilev
authors.forename: N M
authors.initials: NM
authors.lastname: Gal'berstam
authors.forename: I I
authors.initials: II
authors.lastname: Baskin
authors.forename: V A
authors.initials: VA
authors.lastname: Paliulin
authors.forename: N S
authors.initials: NS
authors.lastname: Zefirov
mesh_terms: D006575:Heterocyclic Compounds, 3-Ring / Q000737:chemistry*; D006576:Heterocyclic Compounds, 4 or More Rings / Q000737:chemistry*; D008958:Models, Molecular; D009153:Mutagens / Q000737:chemistry*; D009574:Nitro Compounds / Q000737:chemistry; D011084:Polycyclic Aromatic Hydrocarbons / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship; D012044:Regression Analysis
chemical_list: D006575:Heterocyclic Compounds, 3-Ring; D006576:Heterocyclic Compounds, 4 or More Rings; D009153:Mutagens; D009574:Nitro Compounds; D011084:Polycyclic Aromatic Hydrocarbons