Pesticides have an important role in agriculture since they can prevent large
crop losses which is crucial in order to keep pace with population growth
that is estimated to be 9.7 billion people by the year 2050. The use of
pesticides is not always in accordance with good agricultural practice so
the human and environmental exposure to pesticides are a continuing concern.
Some triazine derivatives are well-known commercially available pesticides
with proven activity. The researchers have task to design and develop new
pesticides with diminished negative influence on environment and humans
together with a good crop protection ability. In order to achieve this, many
computational and artificial intelligence tools have been used along with
extensive experimental work. A group of twenty-one 6-chloro-1,3,5-triazine
derivatives was investigated in the domain of bioconcentration factor, as an
indicator of the bioaccumulation potential of a substance, and multiple
linear regression (MLR)- quantitative structure-property relationship (QSPR)
modeling was conducted. The proposed MLR-QSPR model was extensively
statistically validated in order to provide reliable model for further
re-search work. All conducted procedures, internal and external validation
as well as normality test of residuals indicated good fitness, absence of
systematic error in model development and high predictive ability of the
proposed model.