Aliuska Morales Helguera

TL;DR: This review attempts to summarize present knowledge related to the computational biological activity prediction based in 2D molecular descriptors implemented in the DRAGON software.

TL;DR: Inhibitory activity against aldose reductase enzyme of flavonoid derivatives were modelled using 11 kinds of molecular descriptors from Dragon software and model with four Galvez Charge Indices showed to contain important information on the relationship between the inhibitor structures and its activity.

TL;DR: It was found that the ROCED parameter gets a better balance between sensitivity and specificity for both the training and prediction sets than other indices such as the Matthews correlation coefficient, the Wilk's lambda, or parameters like the area under the Roc curve.

TL;DR: Inhibition of farnesyltransferase (FT) enzyme by a set of 78 thiol and non-thiol peptidomimetic inhibitors was successfully modeled by a genetic neural network (GNN) approach, using radial distribution function descriptors, suggesting the occurrence of a strong dependence of FT inhibition on the molecular shape and size rather than on electronegativity or polarizability characteristics of the studied compounds.

TL;DR: In this work, Quantitative Structure Activity Relationships (QSAR) has been developed to predict the human MAO inhibitory activity and selectivity and showed how several QSAR models can be combined to make better predictions.