The relationship between the chemical
structure of 54 1-aryl-2-(azol-1-yl)ethane derivatives and their antifungal
activities, tested on 30 fungal species, have been examined. Among the 1-keto
derivatives the compounds with a longer alkyl chain at the carbon atom adjacent
to the azole ring as well as the chloro substituent on the benzene ring showed
the highest antifungal activity, meanwhile, a shorter chain at the same carbon
atom led to highest activity among the 1-chloro derivatives. To find the
physico-chemical properties influencing the antifungal activity two series of
molecular descriptors have been calculated applying the Dragon and the Sybyl
VolSurf computer programs. The 3D structures of the compounds to these
calculations were generated with the HYPERCHEM and the SYBYL molecular modeling
programs. From the equation, obtained with stepwise regression using the
antifungal activity data as dependent and the descriptors as independent
variables, it is obvious that the factors describing the chemical structure
itself (Dragon descriptors) have greater influence on the antifungal activity
than the factors responsible for the passive penetration (VolSurf descriptors).
The principal component analysis revealed that the 1-chloro derivatives, which
are equipotent or more potent than the 10 reference azole fungicides, have at
least two action modes.
The uptake of persistent organic pollutants (POPs) from soil by plants allows the development of phytoremediation protocols to rehabilitate contaminated areas. In this study theoretical descriptors have been employed as independent variables for developing quantitative structure-activity relationship (QSAR) models for predicting the bioconcentration factors (BCFs) of POPs in different plants. A quantitative estimation has been given on the molecular properties of POPs in terms of theoretical molecular descriptors that are relevant to the uptake from soil and pharmacokinetic behavior in plants. The study resulted in statistically significant linear regression models developed for the BCF values of 20 polychlorinated dibenzo-p-dioxins/dibenzofurans and 14 polyhalogenated biphenyls in two zucchini varieties based on retrospective data. The parameters have been selected from a set of 1660 DRAGON, 150 VolSurf and 11 Quantum Chemical descriptors. The best regression model (Eq. 1), employing VolSurf, DRAGON GETAWAY and quantum chemical descriptors, displayed the following highly significant statistical parameters: n=27, R2=0.940, SE=0.155, F=392.1, q2=0.922; external validation set: n=7, R2=0.739, q2=0.47, SE=0.338, F=14.2 It is suggested that the QSAR models proposed might contribute to the development of workable soil remediation strategies.