Application of the artificial neural network in quantitative structure–gradient elution retention relationship of phenylthiocarbamyl amino acids derivatives-Reference-Cited by-同舟云学术

Application of the artificial neural network in quantitative structure–gradient elution retention relationship of phenylthiocarbamyl amino acids derivatives

Published:2002-05 Issue:3-4 Volume:28 Page:581-590
ISSN:0731-7085
Container-title:Journal of Pharmaceutical and Biomedical Analysis
language:en
Short-container-title:Journal of Pharmaceutical and Biomedical Analysis

Author:

Tham S.Y.,Agatonovic-Kustrin S.

Publisher

Elsevier BV

Subject

Clinical Biochemistry,Spectroscopy,Drug Discovery,Pharmaceutical Science,Analytical Chemistry

Reference50 articles.

1. Quantitative structure-activity relationships among macrolide antibacterial agents: in vitro and in vivo potency against Pasteurella multocida;McFarland;J. Med. Chem.,1997

2. Quantitative Structure–Chromatographic Retention Relationships;Kaliszan,1987

3. Molecular mechanism of retention in reversed-phase high-performance liquid chromatography and classification of modern stationary phases by using quantitative structure–retention relationships;Kaliszan;J. Chromatogr. A,1999

4. Use of moment of inertia in comparative molecular field analysis to model chromatographic retention of nonpolar solutes;Collantes;Anal. Chem.,1996

5. Estimation of the partitioning characteristics of drugs: a comparison of a large and diverse drug series utilizing chromatographic and electrophoretic methodology;Hanna;Anal. Chem.,1998

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