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QSAR analysis of the time dependent hypotensive activity of 1‐[3‐(4‐substituted phenylthio)propyl]‐4‐(3,4‐substituted phenyl)piperazines using Artificial Neural Networks by Anil K. Saxena; Klaus‐Jürgen Schaper is a Computer Science article available to read on EtoBox.

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## Abstract The quantitative analysis and physicochemical description of time‐ and dose‐dependent __in vivo__ drug effects is problematic as observed effects depend on both pharmacodynamics and pharmacokinetics. These factors depend in a different manner on the physicochemical properties of investigated drugs. Obviously __in vivo__ effects of drug series are governed by highly non‐linear and extremely complicated relationships. The exact form of these relations usually can be given only for some restricted experimental conditions and generally may be assumed to be unknown. As the function relating __in vivo__ hypotensive effects of the title compounds observed in cats, to dose, time and physicochemical properties could not be modelled explicitly, an attempt has been made to train an Artificial Neural Network (ANN) to find the unknown non‐linear relationship between input variables and observed % effect values (raw data, ±20–25% error). Although each drug was applied at only two different doses and “% effect” was observed at few time points, the weights characterising the ANN model enabled the calculation/prediction of (i) complete time‐activity profiles (reflecting mainly pharmacok

Who reads QSAR analysis of the time dependent hypotensive activity of 1‐[3‐(4‐substituted phenylthio)propyl]‐4‐(3,4‐substituted phenyl)piperazines using Artificial Neural Networks?

It is typically read by researchers, students, and practitioners in Computer Science.

Author
Anil K. Saxena; Klaus‐Jürgen Schaper
Publisher
John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Ltd.; Wiley (ISSN 1611-020X)
Published
2003
Language
EN
Field
Computer Science (Physical Sciences)

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