(2)

by implementation of the same formula above (Equation 2).

manner, but also in the terms of its structural diversity. As mentioned previously, this concept was gen­erally based on the hypothesis: the bigger and more diverse is the generated chemical space, the higher is the probability to identify a potential lead by high-throughput screening (HTS). Unfortunately, such a strategy was disappointing for the pharmaceutical industry, since very frequently resulted in an unsat­isfactory cost-benefit ratio (Lahana, 1999; Ramesha, 2000; O’Driscoll, 2004). Over the years, the drug discovery practice has evolved in a direction of swapping such unreasonable strategies with novel, cost­effective, and smart knowledge-based approaches, and the methodology of virtual combinatorial library design (known as combinatorial enumeration) was found as a suitable one (Aronov, 2002). Nowadays, by using this in silico approach, one can rapidly and efficiently generate thousands of novel compounds in a virtual environment that could effortlessly be manipulated.

appropriate set of substituents (Figure 4a). On the other hand, in the reaction-based enumeration, the combinatorial library is defined by using one- or multi-step generic reaction pathway where the variable entities are represented as reactants (e.g., R

The combinatorial enumeration (virtual combinatorial generation) can be regarded as a straightforward procedure of statistical non-repetitive fragmental permutation where each fragment of a given reactants subset “interact” with each fragment of the other reactants subsets within the previously defined molecular scaffold (Figure 4c). Let’s consider a simple reaction system configured of two subsets of reactants R (N

It should be stressed, however, that in cases where a protein-ligand complex is available (experimen­tally determined or assembled by homology modeling), an initial docking validation run is required. This involves reproduction of the spatial orientation and conformation for the co-crystallized ligand that directly reflects the quality of all the settings performed previously. The ligand reproduction assessment usually consists of re-docking of the co-crystallized ligand conformation and its subse­quent comparison to the calculated docking solutions. As judging criteria for the performed ligand reproduction, the all-atom or heavy-atoms root-mean-square deviation (RMSD) value between each calculated docking pose and the co-crystallized ligand conformation is usually calculated. The calcu­lated RMSD values below 2.0 Å are generally accepted and often indicate a successfully performed ligand reproduction (Verdonk et al., 2003). Moreover, if a library comprised of compounds with experimentally-determined in vitro biological activities is on hand (e.g., similar to the one used for QSAR modeling), then it could be proficiently employed in an additional docking validation experi­ment for evaluation of the target’s discriminatory performances – a valuable guideline that indicates the capability of our biological target to effectively discriminate between known active and inactive ligands (Figure 2). It was demonstrated that such a validation is of exceptional importance where one needs to rank two or more analogous protein homology models for their ligands discriminating capa­bilities (Minovski et al., 2013). In that sense, a widely accepted strategy is the so-called enrichment of the library of known actives with an incomparably higher number of decoy molecules (Huang et al., 2006). Here, a decoy is defined as a molecular entity that physicochemically matches the known active compounds, i.e., shares the same physicochemical features, but at the same time is topologically distinct. The main idea behind this concept is to evaluate how good a constructed protein homology model can identify the known actives in the wave of decoy molecules. For that purpose, the “receiver operating characteristic” (ROC) curve methodology proved to be the most suitable one for solving binary classification problems (Triballeau et al., 2005; Hevener et al., 2009). The ROC curve covers the sensitivity (SE) and specificity (SP) of the model, where SE is graphically expressed as a func­tion of (1-SP). These two features one could easily determine from the confusion matrix representing a binary classification (Manallack et al., 2002). Let’s consider a case where a docking-based virtual screening protocol was applied for evaluation of the discriminatory power of two analogous protein homology models to correctly identify active ligands among a large pool of decoy molecules. In the context of binary classification, four classes could be distinguished: true positives (TP = in vitro actives and in silico identified), true negatives (TN = in vitro inactives and in silico rejected), false positives (FP = in vitro inactives and in silico identified), and false negatives (FN = in vitro actives and in silico rejected). Consequently, SE and SP parameters for both homology models can be calculated as follows (Equation 5 and Equation 6):