Following our previous studies [1, 2] we present here the application of the parameters Wi of Turner et al. [3] in the correlation of metal ion toxicity in plants. The experimental toxicity data set was determined in vivo and contains 17 metal ions, of which 8 metal ions are in group (1) (Al3+, Ba2+, Ca2+, K1+, La3+, Li1+, Mg2+, Na1+), 8 metal ions are in group (2) (Ag1+, Cd2+, Co2+, Cu2+, Fe3+, Mn2+, Ni2+, Zn2+), and one metal ion (Pb2+) belongs

Quantitative structure-activity relationship (QSAR) methods have been first explored in 1950s by Professor Corwin Hansh (USA) a founding father of drug design to correlate the molecular structure to biological activity (Hansch and Fujita, 1964; Hansch, 1972). For decades later, QSARs have proven their usefulness to the screening and prediction of toxicity and biological activity of organic chemicals into agrochemistry, pharmaceutical chemistry, toxicology,

The subject of this paper is to present molecular descriptors representing the electronegativity of OMO (occupied molecular orbital) and UMO (unoccupied molecular orbital) quantum molecular states that could be used to obtain information on the mechanism of electron transfer between metal compound and biological receptor. The molecular descriptors that were used suggested that the (s,p) and (dN) metal ions have different mechanisms of interaction

Quantitative Cationic Activity Relationships (QCARs) were developed to predict the toxicity of metal ions from physicochemical properties and natural occurrence levels. In vivo toxicity data for different concentrations of nitrate salts of 17 metal ions were developed based on germination of sunflower seeds (F.1. Helianthus annuus ASunspotB) in distilled water. The EC50 data were reported as the concentration giving 50% inhibition of radicle growth

Developing and validating quantitative cationic–activity relationships or (Q)CARs to predict the toxicity metals is challenging because of issues associated with metal speciation, complexation and interactions within biological systems and the media used to study these interactions. However, a number of simplifying assumptions can be used to develop and validate (Q)CARs to predict the toxicity of metals: The ionic form is the most active form of