A novel approach to molecular negentropy from the point of view of Markov models is introduced. Stochastic negentropies (MEDNEs) are used to develop a linear discriminant analysis. The discriminant analysis produced a set of two discriminant functions, which gave rise to a very good separation of 93.38% of 151 chemicals (training series) into two groups. The total predictability (86.67%, i.e., 52 compounds out of 60) was tested by means of an external validation set. Randić's orthogonalization procedures allowed interpretation of the model while avoiding collinearity descriptors. On the other hand, factor analysis was used to suggest the relation of MEDNEs with other molecular descriptors and properties into a property space. Three principal factors (related to three orthogonal MEDNEs) can be used to explain approximately 90% of the variance of different molecular parameters of halobenzenes including bulk, energetic, dipolar, molecular surface-related, and hydrophobic parameters. Finally, preliminary experimental results coincide with a theoretical prediction when agranulocytosis induction by G-1, a novel microcidal that presents Z/E isomerism, is not detected.