Bioinformatics Computational Biology Molecular Docking

Virtual Screening

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Virtual screening (VS), often also referred to as in silico screening or protein based database screening, is a computational technique used in drug discovery to search libraries of small molecules in order to identify those structures which are most likely to bind to a drug target, typically a protein receptor or enzyme. 

It describes the process of reducing a library of available and/or virtual compounds to a limited number of potentially bioactive structures for a target or a target family using computational techniques. VS comprehends a broad spectrum of methods. These include computational approaches to filter compound libraries according to their structural diversity, drug likeness, ADME properties, presence of 2D- or 3D-pharmacophores and binding capacity for a target protein calculated using automated docking procedures. The aim of VS consists of the rationalization of the drug discovery process by prioritizing compounds for screening and improving hit rates by using computationally filtered compound libraries.


VS scenarios focus on designing and optimizing targeted combinatorial libraries and enriching libraries of available compounds from in-house compound repositories or vendor offerings.

As the accuracy of the method has increased, virtual screening has become an integral part of the drug discovery process. 

Virtual Screening can be used to select in house database compounds for screening, choose compounds that can be purchased externally, and to choose which compound should be synthesized next.



Given a set of structurally diverse ligands that bind to a receptor, a model of the receptor can be built by exploiting the collective information contained in such a set of ligands. These are known as pharmacophore models. A candidate ligand can then be compared to the pharmacophore model to determine whether it is compatible with it and therefore likely to bind. 


Its virtual screening involves docking of candidate ligands into a protein target followed by applying a scoring function to estimate the likelihood that the ligand will bind to the protein with high affinity. Web Servers oriented to prospective virtual screening are available to all. 

Hybrid methods

Hybrid methods that rely on structural and ligand similarity were also developed to overcome the limitations of traditional VLS approaches. This methodologies utilizes evolution‐based ligand‐binding information to predict small-molecule binders and can employ both global structural similarity and pocket similarity. A global structural similarity based approach uses both an experimental structure and a predicted protein model to find structural similarity with proteins in the PDB holo‐template library. 

Virtual high throughput screening (vHTS) is a methodology which identifies drug candidates from extensive collections of virtual compound libraries. This is achieved by using optimal parameters that compute the complementarity of the target receptor with compounds. 

The compound active site is predicted by virtual screening of a compound against hundreds of proteins representing different active site types. Compound active site prediction provides comprehensive effects, including potential targets identification, therapeutic agent identification, and adverse effects prediction.

Machine learning-based methods have been widely used in various fields, such as drug discovery, structural biology, and cheminformatics. Based on high-dimensional data processing capability, they are suitable for virtual screening of large compound libraries to classify molecules as active or inactive or to rank based on their activity levels.

Virtual combinatorial library is a powerful methodology for the discovery of drugs. By virtual combinatorial libraries, novel chemical compounds not available when screening existing compound libraries can be obtained. Virtual combinatorial library design integrates all methods that have been developed for the virtual screening of existing compound libraries. The major methods include similarity-based compound clustering techniques and structure-based docking and scoring.

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