Goodarzi et al. built their QSAR model based on PCA-ANFIS, a hybrid methodology consisting of the principal component analysis and the adaptive neuro-fuzzy inference systems for predicting the anti-HIV reverse transcriptase activities of tetrahydroimidazo [4,5,1-jk][1,4] benzodiazepine derivatives (Goodarzi & Freitas, 2010). In another study, PCA was employed to generate a robust model to predict the Src kinase inhibitory activity of 4-anilino-3-quinolinecarbonitriles (Sun, Zheng, Wei, Chen, & Ji, 2009).

its variants have been employed in many cheminformatics and computational chemistry applications. The followings are some demonstrated examples of its use; 3D-QSAR studies on novel phosphodiesterase-4 inhibitors, pharmacophore identification for benzotriazine derivatives, identification of novel inhibitors for cancer targets, acetylcholinesterase, HIV-1 protease, and ion-channels, as well as developing anti­protozoan compounds (Fernandez, Caballero, Fernandez, & Sarai, 2011; Pourbasheer, Riahi, Ganjali, & Norouzi, 2010; Sahin, Saripinar, Yanmaz, & Gecen, 2011). Evolutionary algorithms such as GA are also successfully applied to descriptor selection and global optimization of QSAR models (Perez-Castillo et al., 2012; Sahin, et al., 2011).

differences between the actual activity

LOO is usually performed for small data set (e.g., 25 compounds), while for big data set, the LMO is preferred. Generally, LMO is considered as a stronger validation approach compared to LOO method due to the fact that in each cycle of LMO method constant perturbation is performed by eliminating

certain number of data points while in LOO method by increasing the number of data points just

of-prediction, however, it should not be regarded as merely criterion of the robustness and predictive ability of the model. Some QSAR practitioners consider the values of q20 5> . are good enough for validity of a model, but in reality, greater values for

original data. Otherwise, the model is not reliable and applicable (Golbraikh & Tropsha, 2002; Tropsha, et al., 2003; Wold & Eriksson, 1995).

1.

2.

4.

Generally, for classification of 3D-QSAR methods one may follow two approaches: alignment­dependent methods and alignment-independent methods. In the former, the calculation of descriptors is strongly influenced by the alignment of the molecules guided by experimental data or computational methods whereas in the latter case, the superposition of molecules is not required.

The most pronounced recent advance in QSAR methodology was the expansion of its dimensionality. These multi-dimensional methods are steadily establishing their own place in QSAR studies by incor­porating extra dimensions, such as physical characteristics, to overcome the obstacles of the existing QSAR methods. Multi-dimensional QSAR approaches are extended forms of 3D-QSAR methods in which additional multivariate molecular descriptors derived from 3D molecular representations, are included. Table 1 shows different multi-dimensional QSAR methodologies and their properties. Hopefinger and his coworkers were the pioneers of 4D-QSAR development (Hopfinger et al., 1997). In 4D approach, the “fourth dimension” also called “ensemble sampling” is determined by including conformational flexibility and the freedom of alignment by ensemble sampling the spatial features such as conforma­tions, orientations, and protonation states for each molecule (Andrade, Pasqualoto, Ferreira, & Hopfin­ger, 2010; de Melo & Ferreira, 2012; Martins, Barbosa, Pasqualoto, & Ferreira, 2009; Polanski, 2009; Vedani, Dobler, Dollinger, et al., 2005). In this method, the bias associated with the choice of bioactive conformation is reduced (Vedani, Dobler, Dollinger, et al., 2005). 4D-QSAR analysis can be used as receptor independent (RI) and receptor dependent (RD) methods. In RI-4D-QSAR method the maxi­mum structural information is obtained through pharmacophoric feature of substituents on the ligands’ scaffold. When the geometry of receptor is known, RD-4D-QSAR analysis is utilized for mapping the ligand-receptor interaction (Damale, Harke, Kalam Khan, Shinde, & Sangshetti, 2014; Polanski, 2009). An example of 4D-QSAR technique, which has been developed based on CoMFA method is “LQTA­QSAR” (LQTA, Laborato´rio de Quimiometria Teo´rica e Aplicada). In this method, conformational ensemble profile is generated for each compound followed by obtaining the 3D descriptors through calculation of intermolecular interaction energy at each grid point considering probes and all aligned conformations obtained from molecular dynamics (MD) simulation (Damale et al., 2014; de Melo & Ferreira, 2012; Martins et al., 2009). Further progress in adding dimensionality to the QSAR techniques was brought about by considering the changes in the conformation of the receptor upon ligand binding (referred to as induced fit effect). Induced fit effect is considered as adaptation of the binding pocket to the individual ligand topology (Damale et al., 2014; Vedani, Descloux, Spreafico, & Ernst, 2007; Vedani & Dobler, 2002; Vedani, Dobler, Dollinger, et al., 2005). This topology alteration in the binding site through induced fit effect may influence the properties of the binding pocket such as hydrophilic, hydrophobic, and dielectric characteristics (Vedani et al., 2007). Addition of another dimension leads to development of 6D-QSAR which takes into account the solvation function as well as those already covered up to the fifth dimension. In 6D-QSAR, different solvation models are incorporated by mapping the surface area with different solvent properties (Damale et al., 2014; Vedani, Dobler, Dollinger, et al.,