QSAR Modeling of Genotoxicity onNon-congeneric Sets of Organic Compounds

Authors:
Uko Maran;Sulev Slid
Affiliations:
Department of Chemistry, University of Tartu, Jakobi Str 2. Tartu 51014, Estonia (author for correspondence, e-mail: uko@chem.ut.ee);Department of Chemistry, University of Tartu, Jakobi Str 2. Tartu 51014, Estonia
Venue:
Artificial Intelligence Review
Year:
2003

Citing 7
Cited 1

An algorithm for the location of transition states

Journal of Computational Chemistry
Calculation of the nonlinear optical properties of molecules

Journal of Computational Chemistry
Indexes of molecular shape from chemical graphs

Computational chemical graph theory
Adaptive floating search methods in feature selection

Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Machine Learning

Machine Learning
Neural Networks for Chemists; An Introduction

Neural Networks for Chemists; An Introduction

Grid computing for the estimation of toxicity: acute toxicity on fathead minnow (pimephales promelas)

GCCB'06 Proceedings of the 2006 international conference on Distributed, high-performance and grid computing in computational biology

Quantified Score

Hi-index	0.00

Visualization

Abstract

A multi-linear (ML) and artificial neural network (ANN) approaches have been used to derive quantitativestructure-activity relationships (QSAR) between the genotoxicity (mutagenicity) and molecular structure of compounds by using large initial pools of descriptors. All derived models involve descriptors that describe possible structural factors influencing the mutagenicbehavior of organic compounds. Different quantum chemical characteristics of compounds have been successfully used together with conventional molecular descriptors. The connection between descriptors represented in the models and the mutagenic behavior ofcompounds is also discussed.