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 (r.griffith@unsw.edu.au)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

Vol. 10, No. 3 2013 Drug Discovery Today: Technologies |

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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