Virtual screening

Virtual screening (VS) 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.[2][3]

Virtual screening has been defined as the "automatically evaluating very large libraries of compounds" using computer programs.[4] As this definition suggests, VS has largely been a numbers game focusing on how the enormous chemical space of over 1060 conceivable compounds[5] can be filtered to a manageable number that can be synthesized, purchased, and tested. Although searching the entire chemical universe may be a theoretically interesting problem, more practical 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.[6][1] 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.


There are two broad categories of screening techniques: ligand-based and structure-based.[7] The remainder of this page will reflect Figure 1 Flow Chart of Virtual Screening.


Given a set of structurally diverse ligands that binds to a receptor, a model of the receptor can be built by exploiting the collective information contained in such 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.[8]

Another approach to ligand-based virtual screening is to use 2D chemical similarity analysis methods[9] to scan a database of molecules against one or more active ligand structure.

A popular approach to ligand-based virtual screening is based on searching molecules with shape similar to that of known actives, as such molecules will fit the target's binding site and hence will be likely to bind the target. There are a number of prospective applications of this class of techniques in the literature.[10][11][12] Pharmacophoric extensions of these 3D methods are also freely-available as webservers.[13][14]


Structure-based 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.[15][16][17] Webservers oriented to prospective virtual screening are available to all.[18][19]

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[20][21] and can employ both global structural similarity and pocket similarity.[20] A global structural similarity based approach employs both an experimental structure or a predicted protein model to find structural similarity with proteins in the PDB holo‐template library. Upon detecting significant structural similarity, 2D fingerprint based Tanimoto coefficient metric is applied to screen for small-molecules that are similar to ligands extracted from selected holo PDB templates.[22][23] The predictions from this method have been experimentally assessed and shows good enrichment in identifying active small molecules.

The above specified method depends on global structural similarity and is not capable of a priori selecting a particular ligand‐binding site in the protein of interest. Further, since the methods rely on 2D similarity assessment for ligands, they are not capable of recognizing stereochemical similarity of small-molecules that are substantially different but demonstrate geometric shape similarity. To address these concerns, a new pocket centric approach, PoLi, capable of targeting specific binding pockets in holo‐protein templates, was developed and experimentally assessed.

Computing Infrastructure

The computation of pair-wise interactions between atoms, which is a prerequisite for the operation of many virtual screening programs, is of computational complexity, where N is the number of atoms in the system. Because of the quadratic scaling with respect to the number of atoms, the computing infrastructure may vary from a laptop computer for a ligand-based method to a mainframe for a structure-based method.


Ligand-based methods typically require a fraction of a second for a single structure comparison operation. A single CPU is enough to perform a large screening within hours. However, several comparisons can be made in parallel in order to expedite the processing of a large database of compounds.


The size of the task requires a parallel computing infrastructure, such as a cluster of Linux systems, running a batch queue processor to handle the work, such as Sun Grid Engine or Torque PBS.

A means of handling the input from large compound libraries is needed. This requires a form of compound database that can be queried by the parallel cluster, delivering compounds in parallel to the various compute nodes. Commercial database engines may be too ponderous, and a high speed indexing engine, such as Berkeley DB, may be a better choice. Furthermore, it may not be efficient to run one comparison per job, because the ramp up time of the cluster nodes could easily outstrip the amount of useful work. To work around this, it is necessary to process batches of compounds in each cluster job, aggregating the results into some kind of log file. A secondary process, to mine the log files and extract high scoring candidates, can then be run after the whole experiment has been run.


The aim of virtual screening is to identify molecules of novel chemical structure that bind to the macromolecular target of interest. Thus, success of a virtual screen is defined in terms of finding interesting new scaffolds rather than the total number of hits. Interpretations of virtual screening accuracy should, therefore, be considered with caution. Low hit rates of interesting scaffolds are clearly preferable over high hit rates of already known scaffolds.

Most tests of virtual screening studies in the literature are retrospective. In these studies, the performance of a VS technique is measured by its ability to retrieve a small set of previously known molecules with affinity to the target of interest (active molecules or just actives) from a library containing a much higher proportion of assumed inactives or decoys. By contrast, in prospective applications of virtual screening, the resulting hits are subjected to experimental confirmation (e.g., IC50 measurements). There is consensus that retrospective benchmarks are not good predictors of prospective performance and consequently only prospective studies constitute conclusive proof of the suitability of a technique for a particular target.[24][25][26][27]

Application to drug discovery

Virtual screening is a very useful application when it comes to identifying hit molecules as a beginning for medicinal chemistry. As the virtual screening approach begins to become a more vital and substantial technique within the medicinal chemistry industry the approach has had an expeditious increase.[28]

Ligand-based methods

While not knowing the structure trying to predict how the ligands will bind to the receptor. With the use of pharmacophore features each ligand identified donor, and acceptors. Equating features are overlaid, however given it is unlikely there is a single correct solution.[1]

Pharmacophore models

This technique is used when merging the results of searches by using unlike reference compounds, same descriptors and coefficient, but different active compounds. This technique is beneficial because it is more efficient than just using a single reference structure along with the most accurate performance when it comes to diverse actives.[1]

Pharmacophore is an ensemble of steric and electronic features that are needed to have an optimal supramolecular interaction or interactions with a biological target structure in order to precipitate its biological response. Choose a representative as a set of actives, most methods will look for similar bindings. It is preferred to have multiple rigid molecules and the ligands should be diversified, in other words ensure to have different features that don't occur during the binding phase.[1]


Build a compound predictive model based on known active and known inactive knowledge. QSAR's (Quantitative-Structure Activity Relationship) which is restricted to a small homogenous dataset. SAR's (Structure Activity Relationship) where data is treated qualitatively and can be used with structural classes and more than one binding mode. Models prioritize compounds for lead discovery.[1]

Machine Learning

In order to use Machine Learning for this model of Virtual Screening there must be a training set with known active and known inactive compounds. There also is a model of activity that then is computed by way of substructural analysis, recursive partitioning, support vector machines, k nearest neighbors and neural networks. The final step is finding the probability that a compound is active and then ranking each compound based on its probability of being active.[1]

Substructural analysis in Machine Learning

The first Machine Learning model used on large datasets is the Substructure Analysis that was created in 1973. Each fragment substructure make a continuous contribution an activity of specific type.[1] Substructure is a method that overcomes the difficulty of massive dimensionality when it comes to analyzing structures in drug design. An efficient substructure analysis is used for structures that have similarities to a multi-level building or tower. Geometry is used for numbering boundary joints for a given structure in the onset and towards the climax. When the method of special static condensation and substitutions routines are developed this method is proved to be more productive than the previous substructure analysis models.[29]

Recursive partitioning

Recursively partitioning is method that creates a decision tree using qualitative data. Understanding the way rules break classes up with a low error of misclassification while repeating each step until no sensible splits can be found. However, recursive partitioning can have poor prediction ability potentially creating fine models at the same rate.[1]

Structure-based methods known protein ligand docking

Ligand can bind into an active site within a protein by using a docking search algorithm, and scoring function in order to identify the most likely cause for an individual ligand while assigning a priority order.[1][30]

See also


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Further reading

  • Melagraki G, Afantitis A, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (May 2007). "Optimization of biaryl piperidine and 4-amino-2-biarylurea MCH1 receptor antagonists using QSAR modeling, classification techniques and virtual screening". Journal of Computer-Aided Molecular Design. 21 (5): 251–67. doi:10.1007/s10822-007-9112-4. PMID 17377847.
  • Afantitis A, Melagraki G, Sarimveis H, Koutentis PA, Markopoulos J, Igglessi-Markopoulou O (February 2006). "Investigation of substituent effect of 1-(3,3-diphenylpropyl)-piperidinyl phenylacetamides on CCR5 binding affinity using QSAR and virtual screening techniques". Journal of Computer-Aided Molecular Design. 20 (2): 83–95. CiteSeerX doi:10.1007/s10822-006-9038-2. PMID 16783600.
  • Eckert H, Bajorath J (March 2007). "Molecular similarity analysis in virtual screening: foundations, limitations and novel approaches". Drug Discovery Today. 12 (5–6): 225–33. doi:10.1016/j.drudis.2007.01.011. PMID 17331887.
  • Willett P (December 2006). "Similarity-based virtual screening using 2D fingerprints" (PDF). Drug Discovery Today (Submitted manuscript). 11 (23–24): 1046–53. doi:10.1016/j.drudis.2006.10.005. PMID 17129822.
  • Fara DC, Oprea TI, Prossnitz ER, Bologa CG, Edwards BS, Sklar LA (2006). "Integration of virtual and physical screening". Drug Discovery Today: Technologies. 3 (4): 377–385. doi:10.1016/j.ddtec.2006.11.003.
  • Muegge I, Oloffa S (2006). "Advances in virtual screening". Drug Discovery Today: Technologies. 3 (4): 405–411. doi:10.1016/j.ddtec.2006.12.002.
  • Schneider G (April 2010). "Virtual screening: an endless staircase?". Nature Reviews. Drug Discovery. 9 (4): 273–6. doi:10.1038/nrd3139. PMID 20357802.
  • VLS3D – list of over 2000 databases, online and standalone in silico tools
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