In the quest for more sophisticated and efficient ways toward the computational design of novel drug candidates, various integrated in silico drug design methodologies are developed and a bundle of useful examples can be found in the literature. A straightforward and frequently utilized in silico integrations is the methodology of extrapolation of a pre-devised QSAR model for selection and/or prediction of the biological activity values for compounds comprising a virtual combinatorial library (Liu et al., 1998; Burden & Winkler, 1999; Agrafiotis, 2000; Andersson et al., 2001; Grzybowski et al., 2002; Cruz-Monteagudo et al., 2008). As demonstrated, principal component analysis (PCA), artificial neural networks (ANNs) or 3D-QSAR models such as the Comparative Molecular Field Analysis (CoMFA) are the most widely exploited as robust in silico screening devices for selection of hit candidates, while genetic algorithm (GA) or desirability-based methods (e.g., implementation of multi-objective optimization for simultaneous mapping of desired drug properties within QSAR models) are commonly applied for ranking purposes (Cruz-Monteagudo et al., 2008; Nicolaou & Brown, 2013). Notwithstanding their favorable outcome in many cases (Grzybowski et al., 2002), it must be taken into account that not every QSAR model is suitable. The lack of validation of the established QSAR model, the wrongly assessed or not assessed model’s applicability domain, or even the use of mis- and/or non-interpretable molecular descriptors are just a few instances which could lead the drug discovery process into a wrong direction (Scior et al., 2009; Bajot, 2010; Sacan et al., 2012). Even in cases where the QSAR model is properly established, its implementation just as a rough in silico filtering tool for selection of drug candidates from a massive pool of possibilities without taking into consideration their druggability properties (Oprea, 2000) could also outcome in disappointingly low hit rates. Moreover, the absence of any structural data for the biological target or even an a priori knowledge for the structure-activity relationship (SAR) between the investigated entities, could additionally contribute to a poor epilogue of the screening strategy.

Conceptually similar, but improved strategies that cover all of these missing aspects or just partially, are the methods of employing three-dimensional (3D) structural concepts such as the ligand-based, structure-based, or their combination as in silico filters for virtual combinatorial libraries (Hecker et al., 2002; Salemme et al., 1997; Shaikh et al., 2007; Krovat et al., 2005; Schlegel et al., 2007; Vilar et al., 2009; Zhang et al., 2013; Drwal & Griffith, 2013). As implied by their name, the availability of 3D structural information for the ligand(s) and/or biological target(s) usually obtained by X-ray crystal­lography or NMR is of critical significance for their practical utilization as in silico ligand filters. More importantly, these methods could also give a structural insight into the possible ligand-protein interac­tions – an invaluable information that indicate to, what might be expected or what will be the next step toward the design of better hits. Over the years, various straightforward, but also advanced approaches have been invented and some of them became a cornerstone of the modern drug discovery. Among them, the ligand-based and its more advanced counterpart the structure-based pharmacophore modeling concept as well as the methodology of molecular docking of ligands into a defined protein binding site are the most commonly utilized for virtual ligand screening (Wolber & Langer, 2005; Wolber & Kosara, 2006; Schlegel et al., 2007).