Editorial [Hot Topic:Multivariate QSAR Methods (Guest Editor: Peter P. Mager Co-Guest Editor: Matheus P. Freitas)]

Increasing efforts to estimate the activity or any other biological property of a given compound have been made since the advent of in silico approaches for drug design. In ligand-based QSAR/QSPR methodologies, descriptors are used to correlate samples set with the corresponding dependent variables (bioactivities, toxicity, etc.), in lieu of testing experimentally the response of a drug-like compound. A large amount of information is usually required or produced to achieve such correlation, and then multivariate analysis has been invoked to manipulate the generated data in order to investigate their variance. Accordingly, chemometric techniques for regression, data exploration and variable selection must be capable to reduce dimensionality and allow the building of predictive models. The present Hot Topic Issue of Current Computer-Aided Drug Design (CC-ADD) provides comprehensive reviews of several multivariate QSAR methods, as well as variable selection and docking studies, covering useful aspects in multivariate modeling applied to drug discovery. In addition to the methodologies used for fast developing computer-aided drug design, their applications and end results are also presented to the scientific community involved in the prediction of drug targets. In this issue, Arodz and Dudek provide an excellent overview of multivariate quantitative structure-activity relationships involving simultaneous modeling of activities toward several related endpoints, and a comparison with univariate models is also given. The authors also focus on neural networks and other non-linear methods to predict all activities simultaneously with good accuracy. In another article, Freitas reviews the various chemical descriptors used to derive QSAR models, from classical to multidimensional predictors. Special emphasis is given to MIA (multivariate image analysis) descriptors, which have shown to exhibit some operational advantages over well established protocols. Remarks and applications of the MIA-QSAR method are presented, and its potentialities and limitations are discussed. Variable selection plays an important role when building a significant QSAR model by selecting important descriptors from descriptors pool. The article of Funatso et al. is focused on this topic, such as genetic algorithm and programming, simulated annealing, and so on. In addition, 3D-QSAR and related methods are presented, and details about alignment and new methods using molecular surface properties are also taken into account. In the fourth article, Mager highlights the use of simultaneous one-regression/one-observation leaving-out resampling regression analysis (SimR) to the selection of suitable chemical descriptors from a pool of variables. This method selects well and shows advantages when compared to GRNN, a combination of generalized regression (GR) and artificial neural networks (ANNs). Additionally, the prediction of protein subdomains as potential molecular drug targets is demonstrated and the protein-ligand docking study exemplified to human P2X7 (h-P2X7) receptor subunit and a series of novel 4,5-diarylimidazoline inhibitors. Overall, I hope that readers would enjoy reading these amazing contributions, expectedly valuable to those involved in the area of drug design and, particularly, in multivariate QSAR.