Artificial neural network prediction of the psychometric activities of phenylalkylamines using DFT-calculated molecular descriptors
Mina Haghdadi, M. Fatemi
Journal of the Serbian Chemical Society 2010 DOI: 10.2298/jsc100408116h (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Theoretical or computational study Peer reviewed |
|---|---|
| Population | 48 phenylalkylamine derivatives |
| Citations | 2 |
| Key points | The work proposes a QSAR approach combining DFT-derived molecular descriptors and an ANN to predict psychometric activity of phenylalkylamines, but the abstract does not report the model's predictive accuracy or specific findings. |
Abstract
In the present work, a quantitative structure-activity relationship (QSAR) method was used to predict the psychometric activity values (as mescaline unit, log MU) of 48 phenylalkylamine derivatives from their density functional theory (DFT) calculated molecular descriptors and an artificial neural network (ANN). In the first step, the molecular descriptors were obtained by DFT calculation at the 6-311G