Virtual screening applications

The most obvious drawback of Fourier space approaches is the computational cost of the Fourier transformation itself. However, this can be circumvented in some virtual screening applications. Gaussian functions are frequently used to approximate electron densities. Interestingly, the Fourier transform of a Gaussian function is again a Gaussian function and hence amenable to analytic transformation. 

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]. 

Virtual screening applications based on superposition or docking usually contain difficult-to-solve optimization problems with a mixed combinatorial and numerical flavor. The combinatorial aspect results from discrete models of conformational flexibility and molecular interactions. The numerical aspect results from describing the relative orientation of two objects, either two superimposed molecules or a ligand with respect to a protein in docking calculations. Problems of this kind are in most cases hard to solve optimally with reasonable compute resources. Sometimes, the combinatorial and the numerical part of such a problem can be separated and independently solved. For example, several virtual screening tools enumerate the conformational space of a molecule in order to address a major combinatorial part of the problem independently (see for example [199]). Alternatively, heuristic search techniques are used to tackle the problem as a whole. Some of them will be covered in this section. 

Enteringthe Real World Virtual Screening Applications 89... 

Liao, C.Z., Karki, R.G., Marchand, C., Pommier, Y., Nicklaus, M.C. Virtual screening application of a model of full-length HIV-1 integrase complexed with viral DNA. Bioorg. Med. Chem. Lett. 2007, 17, 5361-5. 

Considering known SAR, how good are the models for virtual screening applications ... 

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. 

Virtual screening is not a replacement for experimental HTS and is perhaps best viewed as an aid to HTS. Using virtual screening as a prefilter can allow one to select subsets of compounds (focused library) from a larger library and reduces the cost and time required for subsequent experimental screening. Several success stories of virtual screening applications (73) demonstrate the utility of these computational methods for drug discovery, both in academia and industry. 

Bohm HJ, Stahl M (1999) Rapid empiring scoring functions in virtual screening applications, Med Chem Res, 9 445-462... 

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