The Process of Virtual Screening

In this section, we discuss a general strategy of virtual screening based on the 3D structure of a target. Typically, the following steps are typically taken. 

Selection of one or more key interactions that need to be satisfied by all candidate molecules. 

Computational search (by docking and/or pharmacophore queries) in chemical databases for compounds that fit into the binding site and satisfy key interactions. 

Analysis of the retrieved hits and removal of undesirable compounds. 

Once a reasonable binding hypothesis has been generated, the next step is the actual virtual screening. Whether one uses databases of commercially available compounds or virtual libraries of hypothetical chemical structures, it makes sense to dock not just any compound, but only those that pass a number of simple property filters. Such filters remove 

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Note that the process of virtual screening still involves manual interventions at various stages. In principle, the whole process can be carried out in a fully automated manner, but in practice visual inspection and manual selection are still very useful. 

Virtual screening has attracted increasing attention during the last decade as a tool to computationally search for new chemotypes with a specified bioactivity. The role of virtual screening in the research process, however, depends on the availability of other lead identification techniques and on the timing of virtual screening within this... 

The result of computational chemistry is some potential drug candidates. These can be synthesized using combinatorial or wet laboratory techniques, and then tested with assays. Screening an array of ligands virtually is cost effective and compresses the discovery timeline. Exhibit 3.10 shows a typical workflow process for virtual screening. 

The structure based virtual screening process includes also the use of some keywords. With these keywords FLAP fdters out matches and keeps them only if they make sense in terms of binding site shape. FLAP can also allow additional binding site volume (cavity expansion, useful when the protein structure under investigation is an homology model) and with the use of regions (definition of a sphere within each pharmacophore needs to have at least one point) or selection of a probe (enforcing a particular feature to be present in the calculated pharmacophores) certain constraints can also be added. 

The fully automated computational procedure is a valuable ne v tool in virtual screening and in early ADME-Tox, vhere drug safety and metabolic profile patterns must be evaluated in order to enhance and streamline the process of developing neiv drug candidates. 

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