A Network-Based Multi-Target Computational Estimation
Scheme for Anticoagulant Activities of Compounds
Qian Li1,2,4., Xudong Li1., Canghai Li3, Lirong Chen1*, Jun Song3, Yalin Tang2*, Xiaojie Xu1*
1Beijing National Laboratory for Molecular Sciences, State Key Lab of Rare Earth Material Chemistry and Applications, College of Chemistry and Molecular Engineering,
Peking University, Beijing, People’s Republic of China, 2Beijing National Laboratory for Molecular Sciences, Center for Molecular Sciences, State Key Laboratory for
Structural Chemistry of Unstable and Stable Species, Institute of Chemistry Chinese Academy of Sciences, Beijing, People’s Republic of China, 3Experimental Research
Center, China Academy of Chinese Medical Sciences, Beijing, People’s Republic of China, 4Graduate University of Chinese Academy of Sciences, Beijing, People’s Republic
Background: Traditional virtual screening method pays more attention on predicted binding affinity between drug
molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is
often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against
a complex disease by general network estimation has become feasible with the development of network biology and
system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex
disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint
that partial inhibition of several targets can be more efficient than the complete inhibition of a single target.
Methodology: We developed a novel approach by integrating the affinity predictions from multi-target docking studies
with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network
efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes,
while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the
two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined
network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of
argatroban intermediates and eight natural products respectively. The better correlation (r=0.671) between the
experimental data and the decrease of the network deficiency suggests that the approach could be a promising
computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery.
Conclusions: This article proposes a network-based multi-target computational estimation method for anticoagulant
activities of compounds by combining network efficiency analysis with scoring function from molecular docking.
Citation: Li Q, Li X, Li C, Chen L, Song J, et al. (2011) A Network-Based Multi-Target Computational Estimation Scheme for Anticoagulant Activities of
Compounds. PLoS ONE 6(3): e14774. doi:10.1371/journal.pone.0014774
Editor: Jo ¨rg Langowski, German Cancer Research Center, Germany
Received November 18, 2009; Accepted February 19, 2011; Published March 22, 2011
Copyright: ? 2011 Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted
use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This project was supported by the National Science and Technology Major Project 2008ZX09401-006 and 2008ZX09202-007 (http://www.most.gov.cn/).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: email@example.com (LC); firstname.lastname@example.org (YT); email@example.com (XX)
. These authors contributed equally to this work.
The formation of a fibrin clot at the site of an injury to the wall
of a blood vessel is an essential part in stop blood loss after vascular
injury. The reactions that lead to the formation of fibrin clots are
commonly described as the clotting cascade, in which the product
of each step is an enzyme or cofactors necessary for the following
reactions to proceed effectively. The clotting cascade can be
divided into three parts, the extrinsic pathway, the intrinsic and
the common pathway. The extrinsic pathway begins with the
release of tissue factor at the site of vascular damage and leads to
the activation of factor X. The route provides an alternative
mechanism to activate factor X, from the activation of factor XII.
The common pathway is composed of steps linking the activation
of factor X to the formation of a multimeric, cross-linked fibrin
clot. Each of these processes includes not only a cascade of events
that generate the necessary catalyst for the formation of clots, but
also many positive and negative regulatory events.
As a result of advances of computational techniques and
hardware solutions, virtual screening has dramatically speeded up
modern lead identification and lead optimization. Ligand-based
and structure-based virtual screening are two most important
methods used in current computer aided drug design. Ligand-
based methods such as chemical similarity analysis and
pharmacophore modeling mainly focused on the features of
the active ligands structure. With high performance output,
ligand-based virtual screening was widely used to screen large
compound database. However, the fundamental problem of the
methods is that definition of what constitutes an active scaffold is
highly subjective. Synergized with X-ray crystallography, NMR
spectroscopy and isothermal titration calorimetry (ITC), structure-
based virtual screening has been used to complement experimental
PLoS ONE | www.plosone.org1 March 2011 | Volume 6 | Issue 3 | e14774
high-throughput screening (HTS) methods to improve the
efficiency and efficacy of discovering lead inhibitors[7–11].
Structure-based screens typically the molecular docking to fit
small organic molecules into targets of known structure, evaluate
them for structural and chemical complementary. In last few
years, investigators have also turned to predict new substrates for
enzymes or receptors of unknown function (such as the membrane
proteins) and to predicting potent small molecules based on multi-
With emergences of new paradigms in multi-target drug
discovery for several complex diseases, multi-target virtual
screening has been presented and executed to discover the
regimen which could target many different proteins and could
be of low cost, efficacy and better tolerance. However, the
importance and role of target in many complex disease systems
were not explicitly considered in the reported literatures about
multi-target virtual screening. Moreover, as most traditional
virtual screening method, more attention was paid on binding
affinity between drug molecule and target instead of phenotypic
data of drug molecule against disease system.
With the progress of system biology and bionetwork, we know
that the biological potency of an ideal drug may not merely
determined by the inhibition of a single target, but rather by the
rebalancing of several proteins or events, which contribute to the
etiology, pathogeneses, and progression of a complex disease [13–
26]. The available methodologies of in silico screening based on a
single target seem not effective in studying ligands’ effects on
biological process comprehensively for some cases[27,28]. In the
current work, a novel approach was developed by integrating the
predictions based on multi-target docking studies through
biological network efficiency analysis to estimate the biological
potency[26,29–31]. The work flow was shown in Figure 1. The
satisfactory predictions of our model were validated by the
experiments. Similar model to predict the biological potency of
drugs quantitatively by combining the multi-target virtual
screening and biological network calculation together have not
been yet reported in the past references. This novel model could
be a powerful tool for combinatorial drug discovery and the
development of multi-target drugs.
1 Constructions of the docking library
The docking library for multi-target virtual screening against
clotting cascade comprises 1177 compounds from 24 Traditional
Chinese Medicines (TMCs) that were widely used as components
of recipes against cardiac system diseases. These TCMs include 23
original plants and 1 original animal (their information can be
found in the Supporting Information S1). All compounds
identified in these TCMs were collected from Chinese Herbal
Drug Database developed in our group and other litera-
tures[5,33–35]. In addition, some active synthetic compounds
against coagulation cascade available to our laboratory were
included in the docking library, for example, seven argatroban
intermediates. The structures of these compounds were construct-
ed and minimized with the MMFF force field in Discovery
Studio molecular simulation system (DS, Accelrys Inc.). In
minimization, the threshold of root mean square deviation
(RMSD) of potential energy was set to 0.001 kcal?A˚-1?mol-1.
The optimized structures of all compounds were saved as sdf and
mol2 formats, respectively, for further docking study and were
included in the Supporting Information S1.
2 Network-Based dual-step hierarchical Computational
Fourteen proteins authorized as drug targets by US Food and
Drug Administration (FDA)wereused inthe virtualscreening based
on docking simulations. These targets include coagulation factor
Xa, thrombin, coagulation factor IXa, tissue factor:coagulation
factor VIIa complex, coagulation factor VIIa, fibrin, kallikrein,
tissue factor, prothrombin, von Willebrand factor, coagultaion
factor VIII, coagulation factor XI, fibrinogen, and coagulation
factor XIII. To reduce computational cost while not degrade the
calculation accuracy, two docking approaches, including Ligandfit
and Autodock, were successively employed to dock candidates to
the binding sites of these receptors in accordance with the order of
their docking simulation accuracies in network-based dual-step
hierarchical virtual screening. Top ten percent of hits from the
previous step were used for the next step. In every steps of serial
virtual screening, one candidate was estimated and ranked based on
its influence on the network efficiency of clotting cascade network
instead of the scoring functions of these binding poses on one target
as used in conventional virtual screening methods.
(a) Docking and scoring with Ligandfit.
structures of fourteen targets were retrieved from the Protein Data
Bank (PDB entries: 1FJS, 1TA2, 1RFN, 1W0Y,
1YGC, 2HLO, 2ANW, 1TFH, 1K22, 1AUQ,
3CDZ, 2F83, 1FZG and 1GGT). Hetero atoms
were removed from the receptors, and then hydrogen atoms were
added and wrong valence shells were corrected using Discovery
Studio. For receptor/ligand complex with crystal structure, the
binding site was defined as the grid points around the ligand which
were unoccupied by receptor atoms, whereas for a receptor without
crystal complex structure, potential binding sites were found based
on the shape of the receptor. Ligandfit protocol in Discovery Studio
was used to dock ligands into the specified site by the following steps:
(1). conformational search of candidate ligand for docking, (2).
ligand/site shape matching, (3). positioning the selected ligand
conformation into the binding site, and (4) rigid body energy
minimization of the candidate ligand pose/conformation using the
DockScore energy function and updating the saved list of ligands
with the candidate pose. Except maximum poses retained was set to
1, and default values were adopted for the other parameters. The
Piecewise Linear Potential 1 (PLP1) was selected for subsequent
Figure 1. The work flow of our virtual screening approach.
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calculation of network efficiency shift of all compounds based on our
previous work about the comparison of several empirical scoring
program was used for the second step of the dual-step hierarchical
virtual screening because of the better performance of its scoring
function over those of the others for several target proteins.
First, polar hydrogen atoms were added and non-polar hydrogen
atoms were merged by the Hydrogen module in AutoDock Tools
(ADT) for fourteen targets after water molecule were removed.
Then, Kollmanunited atom partial chargeswere assigned. The grid
map of the docking simulation was established by a 61661661
cube centered on the target active site as defined in Ligandfit, with a
spacing of 0.375 A˚between the grid points. When every ligand was
docked to a target, the Lamarckian genetic algorithm were used
optimize the conformation of ligand in the binding pocket. The set
of parameters was listed as following: the size of the population was
150. The number of energy evaluations was set to 1.756107as the
run terminates. For clustering the conformations, the root mean
square deviation tolerance was 2.0. Twenty independent docking
runs were carried out for every ligand. Other parameters were set to
default. For the targets of which crystal complex structures were
determined, every ligand in complexes was picked up and
sequentially docked back into its initial active sites respectively in
order to assess the reliability and accuracy of docking by Autodock
3 Network construction and analysis
(a) Network construction.
using the information from Reactome knowledgebase. The
clotting cascade pathway has been chosen to build the network.
The enzymes which participate in the pathway were proposed as
nodes and arrows between nodes represent the connections. The
direction of the arrow means that the enzyme in the end of the
arrow enhances the formation of the enzyme located in the front.
(b) Network statistics.
The damage induced by the attacks on
the network is characterized by the network efficiency (NE), which is
The network was constructed
defined as the sum of the reciprocals of the shortest path lengths
between all pairs of nodes. Due to a global topological property
of a network which could be applied to measure the integrity of the
network,the networkefficiencywasassumedtobeused as a measure
for drug efficiency. The NE of a graph G is measured by the
shortest paths between pairs of nodes with the expression:
where dij is the length of the shortest path between node i and j and
the sum is over all N(N 21)/2 pairs of nodes with total number N of
nodes in the graph G. If the network is weighted, dij is the path with
the minimum weight. The initial line values of every edge were
arbitrarily set to 10. To give relative network efficiency, this quantity
NE is divided by the initial network efficiency. Thus we considered
the network efficiency of the initial network as 100% and measured
the relative network efficiency after each attack. We have chosen the
clotting cascade network as the network models.
(c) Network efficiency calculation.
was calculated for each compound. The compounds’ effects to the
network rely on the docking scores. We supposed that the
compound could inhibit the target well while the docking scores
were relatively high. For a ligand, we transformed its docking
scores with a target to line values of all directly downstream edges
of the target in the network and then calculated the network
efficiency. In other word, the line values of all edges, which point
to the other targets from this target, were re-assigned based on the
docking score between the compound and the target. The docking
score threshold was set to 0, so any docking score which was
positive was fixed to 0. For each docking target, the ligand with the
highest binding energy was chosen as the reference standard. We
defined that the most potent ligand would knock the target by
99.95%. Therefore, the ligand could make the value of the lines
that come out of the target enzyme as 200. As the reference ligand
docking score should make the line values to 200, the factor 2.3
was used to achieve this purpose. That was because the 2.3th
The network efficiency
Table 1. Results of clotting assays and network efficiency.
DTT ratiosum Decrease of Network efficiency
salvianolic acid a 0.1210.175 0.2960.592 11.78
salvianolic acid b0.375 0.1320.2650.771 11.97
Argatroban intermediate 10.3840.0650.1120.5610.63
Argatroban intermediate 20.3680.0490.1820.599 10.26
Argatroban intermediate 3 0.2430.0810.1260.4511.47
Argatroban intermediate 40.3050.1380.1610.60411.94
Argatroban intermediate 50.1670.0330.1960.39510.9
Argatroban intermediate 60.2940.1220.2310.64812.05
Biological activity results of relative APTT (Activated Partial Thromboplastin Time), PT (Prothrombin Time) and TT (Thrombin Time), sum of the three ratios of times and
calculated decrease of network efficiency after treated of fourteen compounds. The relative ratios were calculated by the sample time minus the relevant vehicle control
time and then divided by the relevant vehicle control time.
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power of 10 is equal to 200 and that was where the factor 2.3 come
out from. Therefore, the ligand could make the value of the lines
that come out of the target enzyme as 200. BEs represents the
binding free energy of the most potent ligand, BE represents the
binding free energy of other ligands, and LV is the line values of
the edges come out of the target in the network. The line values of
the edges which did not come out of the target enzyme were
defined as 10. The line values of the edges in the network were
calculated with the expression:
Therefore, different ligand would show different effect on the
target. For each ligand the network efficiency was then
recalculated using the redefined line values. The network
efficiency of each ligand was ranked by the decrease of the
network efficiency. The more the network efficiency decreases, the
more potent the ligand is. The procedure of network efficiency
calculation was written in C language using Dijkstra Algorithm.
4 Experimental validations
Among these compounds, we chose fourteen compounds which
could be purchased for the further experimental validations. The
compounds used in the experiments were: fangchinoline, folic
acid, rutin, quercetin, liensinine, salvianolic_acid_A, salvianoli-
c_acid_B and six argatroban intermediates. The structures of these
compounds were showed in the table 1.
Activated partial thromboplastin time (aPTT), prothrombin
time(PT) and thrombin time(TT) assays were performed using a
model LG-PABER-I coagulometer (Steellex Scientific Instrument
Company, which also provided the used plasma and clotting
reagents) in compliance with manufacturers’ recommendations.
All test chemicals with the exception of heparin sodium were
solutized and subsequently serial ten-fold dilutions were prepared
with dimethyl sulfoxide (DMSO) to yield a range of concentrations
(12,1.2,0.12,and 0.012610-3 moles?L-1). Heparin sodium solu-
tions (9.06104 I.U.?L-1) were prepared by dissolving in normal
saline (NS). The above series of solutions (DMSO and NS as
references) were diluted with pooled normal human plasma (1:60
vol:vol), and the mixed plasma were evaluated with aPTT, PT and
Figure 2. The network constructed according to the clotting cascade pathway. The red nodes represent the enzymes participate pathway
and the lines between the nodes reflect the relationships between the enzymes of the clotting cascade pathway. The network contained 41 nodes
(enzymes) and 55 edges (relationships between enzymes).
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TT values, which associate with anticoagulant potential of test
chemicals. All reactions were performed in duplicate and data is
expressed in clot time (second).
Results and Discussions
The constructed network (Figure 2) contains 41 nodes and 53
edges (arrows). The nodes cover most of the important enzymes
that participate in the clotting cascade, such as thrombin, factor X,
factor V, TF, etc. We removed each node and calculated the
network efficiency to determine the importance of the enzyme.
The results show that the nodes corresponding to factor Xa,
thrombin and factor VIIIa:factor IXa are identified as the top
three critical targets. The deletion of factor Xa could reduce the
network efficiency greatly, from 17.822 to 8.894. And knock out of
thrombin from the network could reduce the network efficiency
from 17.822 to 10.542. Our predictions are in good agreement
with the reported results[55,56]. For example, the approved drug
Arixtra is a synthetic and specific inhibitor of activated factor X
(Xa) indicated for the prophylaxis of deep vein thrombosis, which
may lead to pulmonary embolism. That means the inhibition of
factor Xa is an ideal way for thrombosis treatment, which is
consistent with our prediction. As a crucial role in physiological
and pathological coagulation, thrombin can be considered a very
successful drug target because numerous direct thrombin inhib-
itors, e.g., Hirudin, Bivalirudin, Lepirudin, Desirudin, Argatroban,
Melagatran and Dabigatran are in clinical use or undergoing
clinical development as antithrombosis agents. In order to test
the clotting cascade network, we randomly deleted one enzyme in
the network and compute the correspondence network efficiency.
Deletion of enzyme with most network efficiency drop could be
considered as the most important targets. The results showed that
deletion of thrombin and Factor Xa would take most effect to the
network efficiency. That meant thrombin and Factor Xa were
predicted as the most important targets by network efficiency
calculation. This prediction was in accordance with practical
knowledge. Therefore, our clotting cascade network could mainly
reflect the real biological process. After the clotting cascade
network testing, docking validations also should be carried out.
To quantitatively compare the differences of the positions and
orientations of five ligands from targets with complex structures
between experimental and computational conformations, all
RMSD (root mean square deviation) between experimental and
computational conformations of these ligands in these complexes
were calculated. RMSD of coagulation factor Xa, thrombin,
prothrombin and tissue factor/factor VIIa are 1.811, 1.890, 1.702
and 1.943, respectively. Among all five values, only RMSD of
factor VIIa is larger than 2 A˚and is 2.489, but it is acceptable after
analyzing the positions and orientations of functional groups in the
ligand. (The details are stated in the Supporting Information S1.)
These results indicate that docking by Autodock program in our
study is reliable and accurate enough for further analyses.
In the experimental section of this study, three clinical used
blood clotting assays: aPTT, PT and TT were carried out to reveal
the biological activities of the 14 test compounds. The activated
partial thromboplastin time (aPTT) mainly reflects the intrinsic
pathway which is part of the clotting cascade. The prothrombin
time (PT) is measure of the extrinsic pathway of coagulation. The
Thrombin Time (TT), is a blood test which measures the time it
takes for a clot to form in the plasma from a blood sample in
Figure 3. Comparison the predicting ability of the network-
based multi-target computational estimation scheme with
single-target docking scoring function. A) The correlation
(r=0.671) between the integrated fourteen compounds biological
activities and the decreases of network efficiency induced by these
compounds. The decrease of network efficiency is calculated from the
multi-target docking scoring. B) The correlation (r=0.648) between the
fourteen compounds biological activities and the docking scores with
coagulation factor Xa. C) The correlation (r=0.602) between the
fourteen compounds biological activities and the docking scores with
thrombin. The biological activities of the fourteen compounds are
illuminated in the Supporting Information S1.
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anticoagulant which had added an excess of thrombin. However,
the three assays could not correlate to any single-target. As the
three assays individually reflect part of the clotting cascade, we
considered sum of the three experimental measurements could
represent the whole effects of clotting cascade. Therefore, we
correlate the network efficiency to the sum of the three
Then, we conducted multi-target docking for fourteen com-
pounds. Each compound was initially docked and then ranked by
thepredictedbinding energytoobtaintheline valuesinthenetwork.
Afterthat, thenetworkefficiencyfor eachcompound was calculated.
We compared the performances of two docking approaches,
Autodock and Ligandfit, and found that the decrease of the network
deficiency based on the predictions given by Autodock can give
better correlation with the experimental data (r=0.671) than that
based on the predictions given by Ligandfit (r=0.47).
In order to test the probability for large scale screening of this
method, we evaluated the runtime of the docking approach and
Figure 4. The Drug-Target network. Circles represent the enzymes in the clotting cascade pathway and the boxes represent the hit compounds
(rutin, salvianolic acid a, salvianolic acid b, fangchinoline, quercetin, liensinine, folic acid). Each ligand is assumed to connect with its target if it can
form strong interactions with the target. Their interactions are expressed by the connecting edges.
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the network efficiency calculation which were the most time
consuming procedures in this method. Take this study for
example, docking procedures for one compound mainly cost less
than 10 minutes per target per CPU thread. Runtime of network
efficiency calculation was relay on the number of targets in the
network. The runtime complexity for the worst-case scenario of
the network efficiency calculation is O(n2) while the ‘‘n’’ is the
number of targets in the network. The network size could be
extended to hundreds of nodes and thousands of edges. That
would large enough for current disease pathway. For the typical
14 target network in this study, calculating the network efficiency
for 14 compounds based on docking data only cost less than 2
seconds on a single CPU core. If our method was running on a
cluster which contain 64 CPUs (128cores, 256 threads), the
throughput could attain to more than 30 000 compounds per day.
Therefore, it is a feasible approach for large scale screening.
To reveal the importance of the network efficiency, we compared
the predictions by the network efficiency analysis based on the
single target docking and those based on the multi-target docking.
When computed by applying the single target docking scores, the
correlation coefficients of the estimated potency and the experi-
mental data for factor Xa and thrombin, which are supposed as
very important enzymes in clotting cascade[56,58,59,60], were
0.648 and 0.602 (Figure 3B and Figure 3C), respectively. However,
the correlation between the predicted network efficiencies and the
experimental data was improved by applying the multi-target
docking scores (r=0.671) (Figure 3A). It suggests that overall
consideration of the contribution of the biological network might be
better than onlyconsiderationof thecontributionofsingle target for
the accurate predictions of the biological activities.The single target
docking cannot capture the biological effects of the ligands
comprehensively, and the multi-target docking is really necessary
to characterize the complicated binding process of ligands with
multiple targets involved in biological network.
Additionally, we analyzed the potency of the hit compounds (In
order to emphasis on the hit compounds screening from
Traditional Chinese Medicine, six argatroban intermediates were
not include in the analysis.) through the network connectivity. In
Figure 4, a ligand is assumed to connect with its target if it can
form strong interactions with the target. The compound rutin,
which connects with 14 targets, is the most potent compound
according to our experiments. Other hit compounds, such as
liensinine and folic acid, which have less connecting neighbors,
show limited biological activities. It seems that the compounds
which can connect with more targets have higher activities
because the potent compounds interact not only with a single
target but also with a series of important targets in the clotting
cascade pathway. Therefore, the technique which combines multi-
target docking and biological pathway network analysis can
predict the effects of ligands to the whole biological pathway more
An analysis of the pharmacology literature was used to assess the
whole homeostasis property of the compound with a larger
decrease value in network efficiency. Previous reports[61,62]
suggested that rutin protected stroke and inhibited thrombosis.
Salvianolic acid B is also confirmed effective on modulating
hemostasis properties of human umbilical vein endothelial
cells. Salvianolic acid A is found protective against cerebral
and myocardial ischemia and reperfusion. These findings
indicate that network efficiency analysis combined with molecular
docking scoring function can be used to successfully screen natural
product databases of potential drugs in silico to identify molecules
with anticoagulant activity.
Generally speaking, current virtual screening methods mainly
focus on single drug-target interaction. The correlation coefficients
between the estimation and the experiment values were based on
compounds’ effects of single target inhibition. However, com-
pounds’ effects on single target inhibition hardly correlated to the
whole effects on such biological pathway process. At the same
time, a few other studies also tried to relate drug effects via
pathway alterations. Mitsos et al. have described a phosphopro-
teomic-based approach to identify drug effects by monitoring
drug-induced topology alterations. They started with a generic
pathway made of logical gates and performed fitting via an Integer
Linear Program (ILP) formulation. While in our study, we
constructed our screening network based on clotting cascade and
applied the Network Efficiency (NE) for ligand efficiency
prediction. Herein, this method reflected the compound’s effects
on the biological pathway and correlated to the phenotype data
which could provide different opinions on pathway based virtual
Like all virtual screening scoring method, our approach has
many advantages as well as some limitations. One of obvious
advantages of the method is that it specifically considers the role of
every target in the whole coagulation cascade process and assigns
the weightiness on every target by biological network analysis. The
other advantage is that the affinity evaluation in the method is not
limited to molecular docking and scoring, as used in this study.
Other binding energy prediction methods could also be used, such
as pharmacophore, quantitative structure-activity relationship or
comparative molecular field analysis. It is also assumed that the
consideration of flexibility of the targets in molecular docking
might improve the accuracy of the network efficiency. The
relevant work how flexible docking and precise binding free energy
computational methods affect the accuracy of the network
efficiency is under way. Given the fact that the x-ray structures
of fourteen enzymes in existing networks have been determined,
molecular docking and scoring function are well suited for the
human coagulation cascade system. A clear disadvantage of this
technique is that its accuracy enormously depends on the
reliability of network construction and the veracity of binding
In summary, we developed a model that combines multi-target
docking and network efficiency calculation for the predictions of
the potency of ligands with reasonable accuracy. The method
integrates the scores given by the multi-target docking scores by
the network efficiency analysis according to the targets’ impor-
tance in a biological pathway or process. The network efficiency
analysis based on the multi-target docking can evaluate the
ligands’ potency more comprehensively than the traditional single
target docking and show better prediction accuracy. It remains to
be determined how the size and complexity the biological network
take effect to the biologically relevant, and the relevant work is
Supporting Information S1
Found at: doi:10.1371/journal.pone.0014774.s001 (0.23 MB
Conceived and designed the experiments: QL LC YT XX. Performed the
experiments: QL XL CL. Analyzed the data: QL XL. Contributed
reagents/materials/analysis tools: QL CL JS. Wrote the paper: QL.
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PLoS ONE | www.plosone.org7March 2011 | Volume 6 | Issue 3 | e14774
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