N. Ghoshal et al., QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP STUDIES USING ARTIFICIALNEURAL NETWORKS, Indian journal of chemistry. Sect. B: organic chemistry, including medical chemistry, 32(10), 1993, pp. 1045-1050
The potential of using back propagation (BP) type artificial neural ne
twork for correlation of biological activity with structural and physi
cochemical descriptors of chemical compounds has been demonstrated. Th
e datasets studied concern aromatic and heteroaromatic nitro compounds
, arylhydroxamic acids and different hexestrol derivatives. The biolog
ical activities like mutagenicity, in vitro 5-lipoxygenase inhibitory
potency and estrogen binding affinities have been correlated with the
physicochemical parameters such as energy of LUMO, hydrophobicity and
Hammett's constant and/or van der Waals' volume. Better quantitative f
it could be obtained compared to previous works using simpler networks
and less computer time. The capabilities of statistical and artificia
l neural network methods for QSAR studies have been compared.