Abstract
QSAR models for chlorinated alkenes were developed between <TEX>$E_{HOMO}$</TEX> and their chlorine and carbon content. The aim is to provide valid QSAR model which is statistically validated for <TEX>$E_{LUMO}$</TEX> prediction. Different molecular descriptors, <TEX>$N_{Cl}$</TEX>, <TEX>$N_C$</TEX> and <TEX>$E_{HOMO}$</TEX> have been used to take into account relevant information provided by molecular features and physicochemical properties. The best model were selected using Partial Least Square (PLS) and Multiple Linear Regression (MLR) led to models with satisfactory predictive ability for a data set of 15 chlorinated alkene compounds.
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