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Applying Cheminformatics to Develop a Structure Searchable Database of Analytical Methods
Published on: June 6, 2025
Neural networks in analytical chemistry
1Department of Chemistry, Sharif University of Technology, Tehran, Iran.
Methods in Molecular Biology (Clifton, N.J.)
|December 11, 2008
Summary
Artificial neural networks (ANNs) offer versatile applications in analytical chemistry, from predicting drug activity and chromatographic behavior to simulating complex spectra. This chapter explores various ANN architectures and their specific uses in diverse chemical analyses.
Area of Science:
- Analytical Chemistry
- Computational Chemistry
- Cheminformatics
Background:
- Artificial neural networks (ANNs) are increasingly utilized in analytical chemistry for complex data analysis and prediction.
- Various ANN architectures exist, each suited for different modeling tasks.
Purpose of the Study:
- To provide an overview of artificial neural network applications in analytical chemistry.
- To detail the development and application of specific ANNs for quantitative structure-activity relationship (QSAR) studies, chromatographic analysis, electrophoresis, and spectral simulations.
Main Methods:
- Development of three-layer artificial neural networks for modeling anti-HIV activity and heparanase inhibitor potency.
- Application of a genetic algorithm-kernel partial least squares combined with artificial neural network (GA-KPLS-ANN) for predicting aromatic sulfonamide activities.
- Utilizing ANNs for modeling retention behavior in chromatography and response surfaces of detection systems.
- Exploring ANNs in electrophoresis for peptide mapping, including simulation of electropherograms.
- Employing ANNs for simulating mass and 13C-NMR spectra of various organic compounds.
Main Results:
- ANNs successfully modeled the anti-HIV activity of HETP derivatives and activity parameters of heparanase inhibitors.
- GA-KPLS-ANN effectively predicted the activities of aromatic sulfonamides.
- ANNs demonstrated utility in predicting chromatographic retention and detector response surfaces.
- ANNs showed promise in peptide mapping via electrophoresis and electropherogram simulation.
- ANNs were applied to simulate mass and 13C-NMR spectra for diverse chemical structures.
Conclusions:
- Artificial neural networks are powerful and versatile tools for a wide range of applications in analytical chemistry.
- ANNs facilitate accurate prediction of chemical activities, chromatographic behavior, and spectral data.
- The integration of ANNs with other algorithms like GA-KPLS enhances predictive capabilities.
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