Virtual screening/assessment

Virtual screening uses computational docking methods to assess which of a large database of compounds will fit into the unliganded structure of the target protein. Current protocols and methods can, with up to 80% success, predict the binding position and orientation of ligands that are known to bind... 

Fields can be utilized in virtual screening applications for assessing the similarity (alignment) or complementarity (docking) of molecules. Two similarity measures have achieved the most attention. These are the so-called Garbo- [195] and Hodgkin indexes [196] respectively. Others are Pearson s product moment correlation coefficient [169] and Spearman s rank correlation coefficient [169]. 

Apart from expressing SAR, there is no validation method that is particularly recommended for this use. Of course, the selectivity of the pharmacophore will definitely facilitate library design and a possible way to assess it is to screen a database of molecules flagged as active and inactive. In this respect, this is rather similar to the virtual screening usage and the ROC curve approach could be used... 

In addition to this pharmacophore hypothesis, although it met only three of the four criteria, model 1 from run 6 was retained. Surprisingly, despite criterion number 2 not being satisfied (RMS= 1.62, r=0.79), this model exhibits a remarkable ability to discriminate between active and inactive compounds as assessed by the ROC curve, AUC=0.95. In contrast, model 1 from run 8 has good statistics (RMS=0.76, r= 0.96) but a lower AUC of 0.87. This illustrates that a good model for activity prediction may not be the best for virtual screening applications. Let us analyze these two pharmacophore hypotheses further. 

Simplistic and heuristic similarity-based approaches can hardly produce as good predictive models as modern statistical and machine learning methods that are able to assess quantitatively biological or physicochemical properties. QSAR-based virtual screening consists of direct assessment of activity values (numerical or binary) of all compounds in the database followed by selection of hits possessing desirable activity. Mathematical methods used for models preparation can be subdivided into classification and regression approaches. The former decide whether a given compound is active, whereas the latter numerically evaluate the activity values. Classification approaches that assess probability of decisions are called probabilistic. 

In the following subsections we address these three points, outlining concepts of assessing the overall druglikenss of molecules, the concentration of subsets of molecules in focused libraries, and the identification of specific leads through structure-based virtual screening techniques. 

Seeding Experiments to Assess Docking and Scoring in Virtual Screening, 318... 

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