Introduction

Virtual screening is the computational or in silico analogue of biological screening. The aim of virtual screening is to score, rank and/or filter a set of structures using one or more computational procedures. Virtual screening is used, for example, to help decide which compounds to screen, which libraries to synthesise and which compounds to purchase from an external company. It may also be employed when analysing the results of an experiment, such as a HTS run.

There are many different criteria by which the structures may be scored, filtered, or otherwise assessed in a virtual screening experiment. For example, one may use a previously derived mathematical model such as a multiple linear regression equation to predict the biological activity of each structure. One may use a series of substructure queries to eliminate molecules that contain certain undesirable functionality. If the structure of the target protein is known, one may use a docking program to identify structures that are predicted to bind strongly to the protein active site. The number of structures that may need to be considered in virtual screening experiments can be very large; when evaluating virtual combinatorial libraries the numbers may run into the billions. It is therefore important that the computational techniques used for the virtual screening have the necessary throughput. This is why it may often be most effective to use a succession of virtual screening methods of increasing complexity [Charifson and Walters 2000]. Each method acts as a filter to remove structures of no further interest, until at the end of the process a series of candidate structures are available for final selection, synthesis or purchase.

Wilton et al. [2003] have suggested that there are four main classes of virtual screening methods, according to the amount of structural and bioactivity data available. If just a single active molecule is known then similarity searching can be performed in what is sometimes known as ligand-based virtual screening. If several actives are available then it may be possible to identify a common 3D pharmacophore, followed by a 3D database search. If a reasonable number of active and inactive structures are known they can be used to train a machine learning technique such as a neural network which can then be used for virtual

General Filters

Substructure Filters

Drug-like Filters ADMET Properties

Actives Known

General Filters

Substructure Filters

Drug-like Filters ADMET Properties

Actives Known

3D Structure of Target Known

3D Structure of Target Known

Ligand-Based Methods

Machine Learning Techniques Pharmacophore-Based Design Similarity Searching

Biological Screening Compound Acquisition Library Design Synthesis

Structure-Based Methods

Protein-Ligand Docking

Biological Screening Compound Acquisition Library Design Synthesis

Figure 8-1. Many virtual screening processes involve a sequence of methodologies.

screening. Finally, protein-ligand docking can be employed when the 3D structure of the protein is known. In addition to these methods that are specific to particular targets, virtual screening techniques have been developed that are of more general applicability. These include, for example, methods that attempt to predict the likelihood that a molecule possesses "drug-like" characteristics and that it has the desired physiochemical properties to be able to reach its site of action within the body. A schematic illustration of a typical virtual screening flowchart is shown in Figure 8-1.

Similarity searching and pharmacophore methods were described in Chapters 5 and 2, respectively. Many of the machine learning techniques that can be applied when several actives and inactives are known were introduced in Chapter 7 and will be further expanded here in the context of predicting "drug-likeness". We will then discuss the application of protein-ligand docking in structure-based virtual screening, and finally the prediction of ADMET properties.

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