New series of 6-substituted-3-arylcoumarins displaying several alkyl, hydroxyl, halogen, and alkoxy groups in the two benzene rings have been designed, synthesized, and evaluated in vitro as human monoamine oxidase A and B (hMAO-A and hMAO-B) inhibitors. Most of the studied compounds showed a high affinity and selectivity to the hMAO-B isoenzyme, with IC(50) values on nanomolar and picomolar range. Ten of the 22 described compounds displayed higher MAO-B inhibitory activity and selectivity than selegiline. Coumarin 7 is the most active compound of this series, being 64 times more active than selegiline and also showing the highest hMAO-B specificity. In addition, docking experiments were carried out on hMAO-A and h-MAO-B structures. This study provided new information about the enzyme-inhibitor interaction and the potential therapeutic application of this 3-arylcoumarin scaffold.

Variable selection is a procedure used to select the most important features to obtain as much information as possible from a reduced amount of features. The selection stage is crucial. The subsequent design of a quantitative structure-activity relationship (QSAR) model (regression or discriminant) would lead to poor performance if little significant features are selected. In drug design modern era, by the means of combinatorial chemistry and high throughput screening, an unprecedented amount of experimental information has been generated. In addition, many molecular descriptors have been defined in the last two decays. All this information can be analyzed by QSAR techniques using adequate statistical procedures. These techniques and procedures should be fast, automated, and applicable to large data sets of structurally diverse compounds. For that reason, the identification of the best one seems to be a very difficult task in view of the large variable selection techniques existing nowadays. The intention of this review is to summarize some of the present knowledge concerning to variable selection methods applied to some well-known statistical techniques such as linear regression, PLS, kNN, Artificial Neural Networks, etc, with the aim to disseminate the advances of this important stage of the QSAR building model.

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