Exploring MIA-QSPR's for the modeling of biomagnification factors of aromatic organochlorine pollutants.

Biomagnification of organic pollutants in food webs has been usually associated to hydrophobicity and other molecular descriptors. However, direct information on atoms and substituent positions in a molecular scaffold that most affect this biological property is not straightforward using traditional QSPR techniques. This work reports the QSPR modeling of biomagnification factors (logBMF) of a series of aromatic organochlorine compounds using three MIA-QSPR (multivariate image analysis applied to QSPR) approaches.

Is conformation a fundamental descriptor in QSAR? A case for halogenated anesthetics.

An intriguing question in 3D-QSAR lies on which conformation(s) to use when generating molecular descriptors (MD) for correlation with bioactivity values. This is not a simple task because the bioactive conformation in molecule data sets is usually unknown and, therefore, optimized structures in a receptor-free environment are often used to generate the MD´s. In this case, a wrong conformational choice can cause misinterpretation of the QSAR model.

Exploring MIA-QSARs' for antimalarial quinolon-4(1H)-imines.

A series of quinolon-4(1H)-imines have been recently discovered as antimalarials, targeting both the exoerythrocytic and erythrocytic stages of the parasite's development stages, which correspond to the phase of clinical symptoms. Endowed with chemical and metabolic stability, the quinolon-4(1H)- imines are thus presented as promissory dual-stage antimalarials. Three versions of multivariate image analysis applied to quantitative structure-activity relationship (MIA-QSAR) methods, namely traditional MIA-QSAR, augmented MIA-QSAR (aug-MIA-QSAR) and color-encoded aug-MIA-QSAR (aug- MIA-QSARcolor), were applied to model the antimalarial activities in this series of compounds.