Works (1)
Updated: July 5th, 2023 15:57
2005 journal article
Prediction of electrophoretic mobilities of peptides in capillary zone electrophoresis by quantitative structure-mobility relationships using the offord model and artificial neural networks
ELECTROPHORESIS, 26(10), 1874–1885.
author keywords: artificial neural networks; capillary zone electrophoresis; electrophoretic mobility; peptide separation and mapping; structure-mobility relationship
MeSH headings : Electrophoresis, Capillary / methods; Neural Networks, Computer; Peptides / chemistry; Peptides / isolation & purification; Regression Analysis; Reproducibility of Results; Structure-Activity Relationship
TL;DR:
A 3–4–1 back propagation artificial neural networks (BP‐ANN) model resulted in a significant improvement in the predictive ability of the QSMR over the MLR treatment, especially for peptides of higher charges that contain basic amino acids arginine, histidine, and lysine.
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