1 - Val Gillet - Ligand-based and Structure-based Virtual Screening
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TECHNOLOGIES
DRUG DISCOVERY
TODAY
Combination of ligand- and structure-based methods in virtual screeningMalgorzata N. Drwal, Renate Griffith*Department of Pharmacology, School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia
Drug Discovery Today: Technologies Vol. 10, No. 3 2013
Editors-in-Chief
Kelvin Lam – Simplex Pharma Advisors, Inc., Arlington, MA, USA
Henk Timmerman – Vrije Universiteit, The Netherlands
The combination of ligand- and structure-based mole-
cular modelling methods has become a common
approach in virtual screening. This review describes
different strategies for integration of ligand- and struc-
ture-based methods which can be divided into sequen-
tial, parallel or hybrid approaches. Although no
thorough performance comparisons between com-
bined approaches are available, examples of successful
applications in prospective and retrospective virtual
screening are discussed. Most published studies use a
sequential approach, utilising well-documented single
methods successfully.
Introduction
Computer-aided drug design is traditionally divided into
ligand1-based and structure-based methods. Ligand-based
approaches are often applied when structural information
on the protein target is scarce and analyse the biological and
chemical properties of a pool of ligands. They include ligand-
based pharmacophores, quantitative structure activity rela-
tionship (QSAR) models as well as similarity calculations
based on physicochemical properties and molecular shapes.
With the increasing identification of biological targets
and their three-dimensional structures, structure-based
modelling approaches such as docking and structure-based
pharmacophores, have gained popularity. In recent years,
*Corresponding author.: R. Griffith ([email protected])1 The term ligand describes a small, organic molecule, usually with a molecular weight
smaller than 500 Da.
1740-6749/$ � 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ddtec.2013
rather than the individual application of ligand- or struc-
ture-based methods, combined approaches have been pro-
posed [1,2]. It has been hypothesised that the integration of
methods with its use of all available chemical and biological
information can enhance the strengths and reduce the draw-
backs of each individual method, thereby resulting in more
successful computer-aided drug design. The current review
gives an overview of recent approaches to combine structure-
and ligand-based methods in virtual screening (VS), with
particular focus on pharmacophore models and docking
methods.
Advantages and disadvantages of ligand- and
structure-based methods
Currently available ligand- and structure-based molecular
modelling methods have been successfully used in VS to
retrieve novel compounds as potential leads in the drug
discovery process. However, despite their successes, all meth-
ods face challenges and problems (as summarised in Fig. 1)
that need to be considered during their application.
One of the first ligand-based methods was QSAR modelling
which attempts to derive a correlation between the physico-
chemical and structural properties of the ligands and their
biological function and potency. The basic hypothesis of
QSAR modelling is that the two- (2D-QSAR) and three-dimen-
sional (3D-QSAR) properties of a set of ligands can be used to
build a statistical model of the biological activity which can
then be used to predict activities of new compounds (as
recently reviewed in [3]). A major drawback of 2D-QSAR
models in drug design is that they do not account for
the location of physicochemical properties in space, whereas
3D-QSAR models are limited by the necessity to know the
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Drug Discovery Today: Technologies | Vol. 10, No. 3 2013
Possible wit
Drug Discovery Today: Technologies
Possible without proteinstructural information; scaffold
hopping; profiling and anti-target modelling
Ligand-basedpharmacophores
2D and 3D similarity
Structure-basedpharmacophores
Docking
Possible without proteinstructural information; simple
and fast
Possible without ligandinformation; entire capability of
protein pocket taken into account;prediction of binding modes;
scaffold hopping; profiling andanti-target modelling
Possible when ligandinformation is scarce; no bias
towards exisiting ligands;prediction of binding modes
Protein structural frameworknot taken into account; lack of
quality training sets, over-fitting possible
Protein structural frameworknot taken into account; bias
towards existing ligands;shape descriptors dependent
on input conformation
Selection of essential featuresnot trivial; need to account fordifferent protein conformations
Oversimplification of scoringfunctions; need to account
for different proteinconformations; highcomputational cost
010111000101
Figure 1. Summary of ligand- and structure-based methods discussed in this review. Green balloons describe advantages, and red balloons disadvantages
of the individual methods.
biologically active conformation and the alignment of active
compounds to generate the model. Furthermore, ligand-based
2D- and 3D-QSAR models are not considering ligand confor-
mations, protein structure and flexibility, or solvation effects.
Another ligand-based approach is the similarity method, a
simple and computationally inexpensive method to retrieve
compounds with similar characteristics to known ligands.
These characteristics are encoded as two- or three-dimen-
sional descriptors, for instance topological descriptors expres-
sing molecular structure in terms of fingerprints or
descriptors expressing the shape of known ligands. Both
two- and three-dimensional methods are successful in VS
and have been able to outperform docking methods when
considering enrichment and computation time for many
targets [4]. However, a major problem with similarity meth-
ods is their bias towards input molecules as well as the
difficulty to decide which input structures to use.
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Pharmacophore models represent the spatial arrangement
of chemical features, such as hydrogen bond donors and
acceptors, of the ligand that are necessary for binding to
its target protein [5]. Ligand-based pharmacophores are 3D-
QSAR models and are important when no structural informa-
tion about the target protein or the ligand’s active conforma-
tion is available. A set of structurally and functionally diverse
ligands with known biological activities is used to build a
pharmacophore model. Ligand-based pharmacophores are
very popular and have been implemented in the main com-
mercially available molecular modelling packages such as
Discovery Studio (Accelrys), MOE (Chemical Computing
Group), Sybyl (Tripos) or Phase (Schrodinger). Pharmaco-
phores have the advantage that they can not only be
used to identify novel active compounds in VS, but also
for profiling and anti-target modelling to avoid side-effects
resulting from off-target activity [6]. Through their feature
Vol. 10, No. 3 2013 Drug Discovery Today: Technologies |
representation, pharmacophores allow the identification of
structurally novel compounds (scaffold hopping) [7]. Pharma-
cophores are relatively simple representations of the proper-
ties necessary for binding and compounds are scored based
only on feature mapping and geometric fits [8]. The interac-
tion strength can be taken into account by adjusting feature
weights. However, this can lead to overfitting and bias of the
pharmacophore to the input structures. Additionally, ligand-
based pharmacophores depend on the availability of a good
training set of compounds manifesting the same binding
mode. In many cases it is difficult to find a functionally
and structurally diverse pool of molecules with quantitative
activity data, such as IC50 values or binding affinities. In
particular, the lack of publications with negative results
hinders the identification of inactive molecules, permitting
in many cases only the development of qualitative common-
feature pharmacophores from active compounds.
Docking is a well-established structure-based method to
investigate the binding mode of small molecules into protein
pockets and a large number of algorithms and scoring func-
tions to assess the protein–ligand interactions are currently in
use [9]. The main advantage of docking is that it uses protein
structural information without being biased towards existing
ligand structures. Protein flexibility can be incorporated into
docking algorithms in various ways, including through side-
chain rotamer libraries, soft docking, ensemble or induced fit
docking [9,10]. However, incorporating protein flexibility
increases computational time significantly and also leads
to possibly higher rates of false positives as more ligands
are able to be docked into the pocket. The major challenge
of docking methods is the scoring of protein ligand com-
plexes. Scoring functions used in docking have to compro-
mise between complexity and simplicity, on the one hand
estimating the free energy of binding as accurately as possi-
ble, on the other hand allowing efficient calculations. Most
scoring functions used today show little correlation with the
actual ligand binding affinity and their results are highly
target-dependent [8]. Moreover, entropic and solvation con-
tributions to ligand binding are mostly ignored.
Structure-based pharmacophores are derived from the
structure of the protein target by investigating all possible
interactions sites in a protein cavity [5,11–14]. Potentially
important interaction sites are identified using either energy-
based or geometry-based methods and translated into phar-
macophore features. Structure-based pharmacophores show
similar advantages to ligand-based pharmacophores
described above. Moreover, they can be used when ligand
information is scarce (e.g. orphan receptors) and they are able
to describe the entire interaction capability of the protein
pocket. On the other hand, structure-based pharmacophores
face the challenge that a binding pocket has a higher number
of potential interaction sites than are normally observed
in protein–ligand complexes. The selection of essential
structure-based pharmacophore features is therefore non-
trivial. Furthermore, taking into account different protein
conformations is necessary which increases computational
costs and complicates the feature selection process.
Combination of structure- and ligand-based methods
It has been postulated that using both ligand- and structure-
based methods on the same biological system is advanta-
geous as it takes into account all possible information [1,8].
The combination of structure- and ligand-based methods can
occur either in a sequential, parallel or hybrid manner and is
possible in most current modelling software packages.
Technology 1: sequential combination of methods
In the sequential approach, different structure- and ligand-
based methods are used in a VS pipeline to sequentially filter
the number of hits retrieved until the number is small enough
for extensive biological testing. Often, computationally inex-
pensive methods like pharmacophore screening are used in
the beginning of the multi-step screening process (prefilter-
ing). As the number of hits decreases, computationally more
expensive methods, in particular docking, can be applied to
further filter the retrieved compounds. Several successful
applications of sequential ligand- and structure-based
approaches have been reported recently. In many cases, hits
retrieved by screening with single or multiple pharmaco-
phores [15–19] are further filtered using druglikeness or
absorption, distribution, metabolism, excretion and toxicity
(ADMET) filters and evaluated using docking into the protein
binding site (Fig. 2, Table S1).
Supplementary material related to this article found,
in the online version, at http://dx.doi.org/10.1016/j.ddtec.
2013.02.002.
Technology 2: parallel combination of methods
In the parallel approach, several methods are run indepen-
dently and the top hits of each method are selected for
biological testing. The methods used should be complemen-
tary and can include pharmacophore models, ligand similar-
ity methods with the application of two- and three-
dimensional descriptors, as well as docking. Benchmarking
studies with retrospective analysis of performance (Table S1)
have shown that the successful application of parallel meth-
ods in VS is possible [20–22]. To our knowledge, until now
only one study illustrates the prospective use of ligand- and
structure-based methods combined in a purely parallel fash-
ion, leading to the identification of novel active compounds
for several targets [21].
Technology 3: hybrid algorithms
Hybrid approaches, which represent a true combination of
structural and ligand information into a standalone method,
have been developed and used successfully.
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Drug Discovery Today: Technologies | Vol. 10, No. 3 2013
Compound database Compound database
Pharmacophores
Property filters
Docking
Manual selection Automated selection, combined scoring
Phar
mac
opho
res
Sim
ilarit
y
Dockin
g
Drug Discovery Today: Technologies
Figure 2. Workflow of combined structure- and ligand-based approaches. Sequential approaches perform different methods on a decreasing number of
compounds (left). Parallel approaches perform different methods independently on the same number of compounds (right).
Protein–ligand pharmacophores represent a combination
of ligand- and protein-information as they are developed
based on experimental structures of protein–ligand com-
plexes. The observed protein–ligand interactions are directly
translated into pharmacophore features. Excluded volumes
are used to restrict filtered compounds to the size of the
binding pocket. Although manual placement of pharmaco-
phore features is possible, automated methods have also been
developed [11,13,23,24]. Protein–ligand pharmacophores
have been successfully applied in VS and recently also for
profiling purposes [25]. Sequential combination of protein–
ligand pharmacophores with other ligand- or structure-based
molecular modelling methods [18,26–28], or parallel combi-
nation of different protein–ligand pharmacophores [29], is
also possible in VS (Table S1). If no experimental structures of
protein–ligand complexes are available, they can be derived
from docking into homology models or experimental struc-
tures [19,30,31]. To account for flexibility of the protein–
ligand complex, dynamic pharmacophores can be developed
from multiple input structures, for example generated in
molecular dynamics simulations [32]. An alternative method
to encode protein–ligand interactions are binary strings
called structural interaction fingerprints. Although this method
is computationally more efficient than pharmacophores,
information about spatial arrangement and interaction
strength is partially lost (as reviewed in [1]).
A different hybrid structure- and ligand-based approach is
the use of pharmacophore models in docking to constrain
poses generated to a specific binding mode. Docking with
pharmacophore constraints or interaction motifs has
been implemented in several docking programs in the last
decade, as reviewed in [2]. Mainly structure-based and
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protein–ligand-based pharmacophores are used as constraints
during the docking run either by mapping ligands to the
pharmacophores (e.g. as described in [32]) or by introducing
a penalty term when interactions are not satisfied.
A third hybrid approach consists of developing pseudor-
eceptors which are an expansion of traditional QSAR meth-
ods that model the receptor structure of the binding pocket
based on the ligand information. Because in this approach
the protein structural information is modelled and not deter-
mined experimentally, and the models depend strongly on
the input ligands and their conformations, this method will
not be discussed further here and the reader is referred to
other reviews [1,33].
Scoring of compounds in integrated approaches
In sequential approaches, the selection of compounds for
biological testing often involves a manual selection step
(cherry-picking) whereby several qualities like docking score,
ligand conformation and orientation, interactions between
ligand and protein, and pharmacophore fit are combined
with chemical intuition and literature-based knowledge
[15–19,26,27]. While the cherry-picking selection method
avoids dependency on one individual ranking measure and
incorporates human experience that cannot be automated, it
also introduces a subjective component into the VS protocol,
thereby complicating comparisons between methods.
On the other hand, because of the large number of mole-
cules to be investigated, manual selection of compounds is
difficult in parallel approaches. Simple parallel selection of
top compounds from each individual method has
been shown to perform robustly across a variety of datasets
and top compound fractions [20,22]. Automated selection
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methods using combined scoring are also being explored. The
scores of integrated methods can be combined in different
ways using data fusion algorithms, which have been mainly
investigated in similarity methods [34], however can also be
applied to combined ligand- and structure-based approaches.
A well-performing probabilistic basis for the selection of
compounds is provided by the calculation of a cumulative
belief, a quantitative expectancy for biological activity of a
compound [21]. Probabilities of retrieving active compounds
can be assigned to each method by assessing the fraction of
actives retrieved in a retrospective analysis. The cumulative
belief is then calculated by combining the probabilities for
each method. Simple addition or averaging of the ranks from
each individual method proves to be the worst performing
parallel scoring scheme which can be explained by the fact
that this scheme is easily affected by an outlying score of an
individual method [20,21].
Assessment of performance of integrated approaches
In many studies, the performance of VS methods is evaluated
retrospectively by investigating the enrichment and area-
under-the-curve (AUC) of hit lists retrieved from a database
seeded with active molecules (decoy database). A similar ana-
lysis can be executed to investigate the performance of inte-
grated structure- and ligand-based approaches versus
individual methods. In a study by Swann et al., the enrich-
ment of hit lists retrieved with different methods for 18
different protein targets has been examined and in most
cases, docking and similarity methods combined in a parallel
fashion were able to outperform individual methods [21].
Similar results were obtained by Svensson and colleagues,
who combined docking, similarity methods and pharmaco-
phores in parallel [20]. When using a sequential combination
of docking with protein–ligand pharmacophore- and simi-
larity-based post-filtering, Planesas et al. showed that a com-
bination of methods can improve overall performance in
finding VEGFR-2 inhibitors, in particular the enrichment
rates in the top fractions of the hit list (early enrichment)
[28]. Although enrichment analyses are widely used, they
encounter several problems that affect their value [35]. They
depend heavily on the database used, for example on the ratio
of active to inactive molecules and the chemical diversity of
the compounds. Moreover, the inactive molecules have often
not been tested against the biological target and truly active
molecules might be hidden in the decoy set. Because the
main aim of VS should be the retrieval of structurally diverse
compounds, chemotype enrichment has been suggested as a
more appropriate retrospective performance measure in VS
methods. A comparison of docking and structure- and pro-
tein–ligand-based pharmacophores in terms of chemotype
enrichment using parts of the directory of useful decoys
(DUD) [36] has shown that pharmacophore-based methods
are superior to docking and that combining structure-based
with protein–ligand-based pharmacophores can increase the
number of chemotypes retrieved [14].
While a retrospective analysis might present encouraging
results, this does not mean that the method will perform well
in VS to retrieve new hits which might have completely
different characteristics from the test molecules of the decoy
database. Ideally, the performance of VS methods and their
combinations should be assessed prospectively, by determin-
ing the hit rate of the database search, the chemical diversity
and the usefulness of the hits in the drug discovery process.
However, a problematic aspect of this assessment is the
definition of a ‘hit molecule’ which is target-dependent
and can range from high-micromolar to sub-micromolar
IC50 or affinity values. Hit rates are also difficult to compare
because they depend on the database being screened, its size
and chemical diversity [35], as well as the availability of
compounds for testing. As summarised in Table S1, recent
studies show a variety of hit rates, ranging from 0.08 to 100%
success. Even more difficult is the evaluation of how useful
the VS approach has been in the context of the drug discovery
process, especially given the different goals and available
resources of industrial and academic investigators. Leach
et al. [5] have addressed some of these issues in their recent
review. Taking into consideration the difficulties of prospec-
tive evaluations of VS results, it is not surprising that no
comparison of different combinations of ligand- and struc-
ture-based methods has been published so far.
Conclusions
In recent years, many successful applications of combined
ligand- and structure-based methods have been described. In
most cases, only a small number of compounds (<50) need to
be tested to allow the retrieval of hits. A comparison of
different combination approaches (Table 1) revealed that
most published studies use a sequential combination of VS
methods. This is, in our opinion, mainly because of the fact
that the numerous successful applications of the sequential
approach are encouraging other researchers to use similar
methodology. Furthermore, input of human expertise for
compound selection is facilitated, and the use of hierarchical
VS allows efficient computation. Parallel approaches, on the
other hand, have been rarely applied in VS. However, the
results of several benchmarking studies as well as the ability
to fully automate the screening and compound selection
process should encourage application of parallel methods
in future. A problem with parallel approaches is the selection
of which methods and how many to combine to achieve
good, non-redundant results [20]. Hybrids of ligand- and
structure-based methods have been developed during the last
decade and are implemented into current modelling software
packages, thus making them standalone methods. Although
division of combined ligand- and structure-based methods
into sequential, parallel and hybrid approaches has been used
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Drug Discovery Today: Technologies | Vol. 10, No. 3 2013
Table 1. Comparison of approaches to combine ligand- and structure-based methods
Approach Examples Comments
Sequential Hierarchical VS: pharmacophore
screening, application of property
filters (druglikeness, ADMET), docking, manual selection
� Computationally expensive methods are used at the end, on a
small number of compounds
� Input of human expertise possible
� Most common strategy
� Has been successful for many different targets
Parallel Parallel application of pharmacophores,
similarity methods, docking, followed
by automated selection
� Careful selection of independent methods necessary
� Fully automated setup and scoring possible
� Promising benchmarking results
Hybrid Protein–ligand pharmacophores,
docking with pharmacophore constraints
� Integration of ligand- and structure-based concepts in one method
� Can be combined with other methods in sequential or parallel fashion
in this review for clarity, combinations of partly sequential
and partly parallel algorithms are possible, also including
hybrid methods, and have been used in VS.
The remaining challenge in drug development is not to
find hits, but to advance them into lead compounds by
predicting their metabolism and adverse effects. By combin-
ing structure- and ligand-based methods, modellers are hop-
ing to address this challenge and to enhance accuracy and
performance of current modelling techniques. Problems still
faced in computer-aided drug design include taking into
account large-scale protein flexibility, the role of solvation,
the accurate calculation of binding energies, and the inves-
tigation of interaction networks beyond pairwise interactions
[37]. Because such calculations are time-consuming and can
be applied only to a small number of selected hits, sequential
approaches might provide an adequate framework to address
these challenges.
Conflict of interest
The authors have no conflict of interest to declare.
Acknowledgements
MD acknowledges financial assistance from the University of
New South Wales, Australia, in providing a PhD scholarship
in the form of a University International Postgraduate Award
(UIPA) as well as the Translational Cancer Research Network
(TCRN) Australia for providing a Postgraduate Research Scho-
larship Top-up in 2012.
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