virtual screening is a well - established 
 hit finding strategy in 
 drug discovery , whereby scientists with 
 access to large compound libraries cherry - pick compounds for high - throughput 
 screening .
vast collections of chemical 
 structures available from compound vendors can also be screened in 
 silico to obtain a short list of compounds for purchase and testing . generally , the aim of virtual screening campaigns is to explore 
 and maximally exploit the available chemical space at minimum cost 
 to identify virtual hits that can be experimentally confirmed and 
 followed up as part of a drug discovery program .
while the number 
 of experimentally validated hits retrieved is an important component 
 of virtual screening campaign success , an equally important , and often 
 neglected , component is the chemical diversity of such hits .
a sufficiently high number of diversified hit 
 scaffolds will , most likely , provide a more solid foundation for a 
 drug discovery program and mitigate the risk of chemical series - specific 
 issues such as poor physicochemical property space , challenging synthetic 
 accessibility , poor selectivity , adverse metabolism , or toxicity profiles . 

computational chemists rely on a number of different families of algorithms 
 to identify novel hits , which mainly differ in the level of chemical 
 abstraction , protein and/or ligand awareness , and computational expense . 

while this practice can provide valuable insights into the different 
 methodologies used in virtual screening , it has intrinsic limitations 
 that prevent scientists from drawing definitive conclusions on the 
 best computational strategy to approach a novel target .
methodological 
 bias may come from the expertise of the users , from the use of decoys 
 ( rather than inactives ) , from difficulty in assigning the appropriate 
 parameters for the methods applied , and from the potential error in 
 data sets and protein structures .
furthermore , performing truly prospective studies in a fair and 
 unbiased manner is challenging because the structures of the known 
 hits are , to some extent , influenced by the choice of protein 
different computational tools show complementary 
 features and are used frequently in combination to optimize virtual 
 screening cascades . in this study , we investigate the use of 
 ligand - based similarity 
 to enhance the performance of ligand docking in virtual screening .
it has long been recognized that docking success 
 rate increases with the similarity of the screened molecule to the 
 ligand bound to the protein conformation under test ( herein described 
 as the native ligand ) . as the structural biology community 
 continually increases exemplification of the druggable genome with 
 protein x - ray crystal structures , drug discovery programs
more frequently 
 have access to multiple structures , and it is important to understand 
 which structures are likely to be most applicable to virtual screening . 

the hypothesis under test is that ligand - based similarity methods 
 can be applied to identify the most appropriate protein structures 
 available to a virtual screening campaign .
this approach has been 
 previously suggested by sutherland et al . , who showed that using a protein structure selected based on the 
 similarity to the crystallographic ligand reduces the success rate 
 gap between native docking ( docking the native ligand back into the 
 apoprotein structure derived from that ligand ) and cross docking ( docking 
 a non - native ligand into the protein structure ) .
in this study , we expand this analysis to three different docking 
 protocols differing in accuracy and speed , as well as to different 
 external data sets representative of virtual screening scenarios . 

the comparisons were performed on the set of native ligands , on a 
 set containing confirmed actives and decoy ligands from the directory 
 of useful decoys ( dud ) data set , and on 
 a published set of active and inactive ligands for the protein under 
 study ( glaxosmithkline ; gsk ) . for cross - docked 
 ligands , it was possible to compare the docking poses with the original 
 native poses by calculation of the root - mean - square deviation ( rmsd ) 
 geometric distance in ngstrms (  ) between the 
 poses . however , this was not possible for the dud and the gsk data 
 sets .
we hypothesized that docking a molecule into the protein structure 
 derived from its most similar crystallographic ligand would be closer 
 in success rate to native ligand docking than to cross docking .
we 
 found that this approach significantly enhances the enrichment of 
 active compounds within larger data sets compared to docking on a 
 single protein structure .
furthermore , this approach reduces the computational 
 expense with minimal reduction in success rate compared to ensemble 
 docking ( which in this work refers to cross docking applied to all 
 the available protein conformations except for the native one ) .
we 
 also demonstrate how molecular modelers could increase the diversity 
 of virtual screening hits while minimizing loss of accuracy .
the protein target cyclin - dependent kinase 2 ( cdk2 ) has previously 
 been used as a model system in numerous cross docking and virtual 
 screening studies ; multiple high - quality crystallographic structures are available 
 from the protein data bank ( pdb ) representative of a diversity of 
 ligand space for cdk2 .
overall , the focus of this study is to 
 identify the best practice 
 that uses the available ligand and structure data and not to compare 
 different computational methods for docking and similarity calculations . 

therefore , the algorithms applied in this study are widely adopted 
 as standards for fingerprint similarity ( pipeline pilot extended connectivity 
 fingerprints ) , three - dimensional ( 3d ) 
 ligand - based similarity ( rocs tanimoto combo ) , and docking ( glide ) .
the choice 
 of the software does not reflect our absolute preferences and represents 
 some of the commonly used virtual screening tools from the scientific 
 literature .
the results from the docking experiments were analyzed 
 using statistical 
 measures , accuracy and enrichment , computational time , and chemical 
 diversity of the identified hits .
limitations regarding the biases 
 previously discussed for virtual screening studies also apply to this 
 study ; however , we believe that these results offer informative insights 
 for computational scientists approaching a novel drug discovery project 
 to make the best use of the data available .
crystallographic pdb 
 structures were selected if they were published after 2010 with a 
 resolution better than 2  .
the ligands of these 92 structures 
 were extracted and their morgan fingerprints generated using the rdkit 
 implementation ( number of bits = 1024 , radius = 2 ) available in knime 
 v2.6.3 for mac .
the fingerprints were processed 
 using the rdkit diversity picker to select 40 representative ligands ; 
 the tool is based on the maxmin algorithm as presented by ashton et 
 al .
each of the selected 40 structures 
 was visually inspected and considered acceptable only if the ligand 
 was present in a single conformation , a hinge binder , and exclusively 
 interacting with the protein pocket and/or with water molecules .
structures 
 that failed to pass this further screening were replaced by the closest 
 analogues not included in the original selection using the tanimoto 
 similarity of the morgan fingerprints .
the pdb codes of the selected 
 structures can be found in table s1 of the supporting 
 information .
the mean pairwise similarity using ecfp_4 fingerprints 
 observed for the set of crystallographic ligand was 0.20 , which indicates 
 a sufficiently high structural diversity .
several authors 
 have reported a strong dependency between virtual screening performance 
 and the data sets used . in this study , 
 a standard
data set was used for cdk2 virtual screening studies ( dud 
 data set ) and a data set recently disclosed by glaxosmithkline scientists 
 extracted from chembl ( gsk ) .
compounds available in the dud data set are either binders or decoys 
 that have similar physicochemical properties to the binders and can 
 be reported in different protonation states .
after structure standardization 
 and removal of duplicates using moe extensions for knime , 1829 compounds remained for cdk2 in the dud 
 data set , of which 50 were actives .
the gsk data sets originally included 
 367 compounds reported with percent inhibition data for cdk2 at two 
 concentrations and measured using two different protocols ( four data 
 points for each molecule ) .
the four single - point data sets showed 
 comparable outcomes ; the data sets were harmonized and reduced to 
 24 actives and 270 inactives as follows : inactive compounds were defined 
 as those with less than 10% inhibition , while the actives were those 
 compounds with greater than 20% inhibition .
compounds were excluded 
 where the outcome of the four assays was not unanimous ( 72 compounds ) .
the three data sets ( dud , gsk , native ligands ) were standardized 
 ( stereochemistry , keep largest fragment ) using the standardize molecules 
 component in pipelinepilot v8.0.1.500 for windows ; the molecules were 
 minimized using corina in pipeline pilot .
compounds to be docked were 
 further processed in knime using the schrdinger tools tautomerizer 
 ( maximum of two tautomers ) and epik ( solvent h2o , predicted 
 states at ph 7  1 ) . for compounds 
 to be used for 3d similarity search ,
25 conformations were produced 
 using omega v2.4.6 ( openeye ) for mac with standard options ( except 
 for strictstereo false , to allow molecules with unspecified 
 stereocenters to be processed ) .
for 
 the molecules in the three data sets , the tanimoto fingerprint 
 similarity to the crystallographic ligands was calculated using pipilinepilot 
 extended connectivity fingerprints ( ecfp_4 ) .
3d similarity was calculated 
 using rocs v3.1.1 ( openeye ) with standard options ( tanimoto combo 
 similarity ) .
ligand 
 complexes were inspected manually to assign the appropriate protonation 
 state and bond order ; the process was guided by chemical judgment 
 and by the data reported in the original publications .
the pdb structures were prealigned using the 
 knime node align binding sites ( schrdinger ) .
the grids were produced using the knime node glide grid 
 generation ( schrdinger ) after preparing the proteins with 
 the ( schrdinger ) knime node protein preparation wizard .
docking was performed using the knime node glide 
 ensemble docking ( schrdinger ) , with three different protocols 
 ( htvs , sp , and xp ) , docking flexibly , penalizing the nonplanar conformations 
 of amide bonds , and adding epik state penalties to the docking score . while in the first part of the study ( native 
 docking and cross docking experiments ) , three different binding hypotheses 
 were produced and compared to the crystallographic ligand conformation
; 
 for the remaining part of the study , only the pose with the highest 
 score was considered .
the rmsd of the heavy atoms of the docked ligand 
 from the prealigned crystallographic ligand was calculated using the 
 knime node rmsd ( moe ) .
the docking experiment 
 was considered successful if the resulting pose had a rmsd of less 
 than 2  from the crystallographic ligand .
this criterion was 
 selected because it has been used by a number of other investigators 
 in recent studies .
data were elaborated and plots were produced 
 using the standard knime nodes , microsoft excel office 2011 ( bar charts ) , vortex v2013.08.24252 ( boxplots , scatter plots , pie chart plots ) and 
 rstudio v0.98.484 ( package enrich vs v0.0.5 for calculating auc and 
 bedroc at  = 20 ) . an exhaustive description 
 of the bedroc metric and its utility for comparing virtual screening 
 methods has been previously presented by truchon and bayly .
a number of data failed to be produced using 
 the methodology described above , particularly docking and 3d similarity . 

however , greater than 96% expected data points were produced ; hence , 
 the reasons for calculation failures were only marginally investigated . 

calculation failures in the docking experiments were mainly due to 
 the lack of good solutions identified by algorithms ( as demonstrated 
 by the higher number of missing values when using the low precision 
 protocol htvs ) and to the mismatch between the protonation states 
 calculated by the software and through the manual inspection step , 
 which made it impossible to calculate the rmsd in cross - docking experiments .
during virtual 
 screening campaigns , docking is often used to provide binding mode 
 hypotheses for compounds with unknown activity against the target 
 of interest and to score these binding hypotheses . for this approach 
 to be successful , two fundamental requirements must be met : the ligand 
 must fit into the non - native protein conformation and high docking 
 scores must be associated with poses that are close to those experimentally 
 observed . in this study ,
glide was used to dock 40 ligands into 
 their native cdk2 protein conformation ( native docking ) and also into 
 the remaining 39 protein conformations ( cross docking ) . in order to 
 produce an rmsd value for cross - docking experiments
the experiment was repeated using three 
 different docking options , which mainly differ in the balance of accuracy 
 versus speed .
htvs docking may be used to screen a million compounds 
 in approximately 17 days using a single processor .
this speed range is applicable to a docking - based virtual 
 screening campaign using several thousands to millions of virtual 
 compounds .
sp docking is the same as htvs in terms of scoring function 
 but uses a more exhaustive conformational sampling , with an estimated 
 10-fold increase in the computational time required compared to htvs .
finally , xp is based on a different scoring 
 function and different criteria for the conformational search component . 

this option is the slowest ( 240 times slower with respect to htvs ) 
 and the most prone to penalize the lack of conformational complementarity 
 between ligand and protein .
results 
 for the cross - docking experiments are reported as the percentage 
 of successful poses observed , where success is measured as an rmsd 
 of below 2  between the crystallographic and docked poses and 
 is referred to as docking success rate ( figure 1 ) .
we observed no significant difference between considering one 
 or three poses resulting from a docking experiment ; the rmsd associated 
 with the highest scoring pose is , in most cases , the lowest or comparable 
 to the lowest .
hence , using one binding pose during virtual screening 
 campaigns was accepted as a reasonable approximation .
glide 
 docking consistently demonstrated a 2-fold ( 2.19  0.07 ) 
 reduction in the docking success rate in cross docking with respect 
 to native docking ; this was independent of the docking protocol adopted . 
 the two most accurate docking protocols ( sp and xp ) showed comparable 
 success rate in native docking ( sp = 87.5% and xp = 82.5% ) and in 
 cross docking ( sp = 40% and xp = 38.7% ) , while the docking success 
 rate for htvs was significantly lower ( 50% in native protein conformations 
 and 22% in cross docking ) .
overall , the approximately 10-fold increase 
 in computational time from htvs to sp increases the docking success 
 rate by greater than 30% in native docking and greater than 15% in 
 cross docking .
docking success rate for three glide protocols : htvs , sp , and xp . 
 docking pose prediction is considered correct if the rmsd from the 
 crystallographic ligand is below 2  .
unfortunately , in a prospective application , the user will 
 have 
 no knowledge of the rmsd , and the success of the docking experiment 
 must be judged based on the docking score alone .
therefore , the distribution 
 of the successful docking experiments was analyzed with respect to 
 the docking score range , where a lower docking score suggests a better 
 docking pose , and the docking success rate observed in each of these 
 ranges ( figure 2 ) .
for this data set , it appears 
 that across the three docking protocols the docking success rate is 
 greater than 50% when the docking score is better than 8 and 
 over 70% when the score is better than 9 .
glide sp and xp 
 produce a similar fraction of accurate poses with scores better than 
 8 ( 4748% ) , while this percentage is only 24% for htvs . 

using high - speed docking seems to reduce the fraction of successful 
 docking poses ( rmsd below 2  ) associated with docking score 
 better than 8 by one - half .
distribution of the relative percentage 
 of the accurate docking 
 experiments with respect to the docking score interval ( upper plot ) . 

variation of the docking success rate with respect to the docking 
 score interval ( lower plot ) .
the 
 results discussed so far highlight two key points : ( 1 ) the conformational 
 complementarity between ligand and protein improves the docking success 
 rate .
( 2 ) more thorough ligand conformational sampling also improves 
 success rate while increasing the computational time . considering 
 the time required for docking calculations and results analysis , in 
 addition to the limitations related to commercial software licenses , 
 it is prohibitive to apply a docking - based virtual screening campaign 
 to screen a large library of compounds ( e.g. , all commercially available 
 compounds ) based on more than one crystal structure .
hence , if several 
 crystal structures are available before starting a virtual screening 
 campaign , it is important to identify the most appropriate method 
 to select the optimal structure .
there is a relationship between the 
 similarity to the crystallographic ligand and the probability of successful 
 docking , as suggested by our observed difference in docking success 
 rate between native docking and cross docking and consistent with 
 previous work in this area .
therefore , docking compounds that belong to a congeneric 
 series for which a crystal structure is available is approximately 
 as accurate as native docking . at the opposite end of the spectrum , 
 compounds that are highly dissimilar to the native ligand
are more 
 likely to induce unexplored conformational changes in the protein 
 structure ; hence , the probability of docking success is lower .
cross - docking experiments were performed using 39 structures , and 
 the results compared to those obtained with docking on a single structure 
 selected using ligand - based similarity ( either ecfp_4 or rocs tanimoto 
 combo , to native docking , and to ensemble cross docking ( based on 
 39 non - native protein conformations , the selected pose is the one 
 associated with the lowest docking score ) ( figure 3 ) .
these experiments were conducted with glide htvs and sp 
 docking protocols ( xp was not considered here because our data suggest 
 that this option does not offer advantages over the faster sp in virtual 
 screening ) . from figure 3 ,
the following observations 
 can be made :   for glide sp , selecting a specific protein 
 structure for 
 cross docking based on ligand similarity does not yield an inferior 
 success rate with respect to performing cross - docking experiments 
 on all the protein structures available and selecting the docking 
 pose corresponding to the best docking score .
for glide 
 sp , ligand - based methods for selecting the protein 
 structure used in docking experiments appear to perform comparably 
 well for this data set ; for glide htvs , the selection guided by ecfp_4 
 fingerprints performs slightly better .
in each case , 
 the median rmsd of experiments based on a 
 specific protein conformation , selected by ligand - based similarity 
 methods , is lower than the median rmsd observed for cross docking ; 
 in most cases , the difference is greater than 1  .
glide sp , the medians of native docking , ensemble cross 
 docking , and docking on a single protein conformation selected using 
 ligand - based similarity are all below 1.3  , which is less than 
 half of the median rmsd observed in cross - docking experiments ( 2.66 
  ) .
ensemble cross docking using glide sp is more 
 accurate 
 than native docking using glide htvs .
thus , suboptimal ligand conformational 
 sampling , the only difference between glide htvs and sp , affects docking 
 success rate more than the use of a suboptimal protein conformation 
 ( cross docking versus native docking ) . when using ensemble docking 
 with glide htvs , the ligand conformations explored increase by a factor 
 of 39 for this data set ( number of non - native proteins conformations ) , 
 while additionally including multiple relevant protein conformations . 
 as a result ,
when using glide htvs , ensemble docking is more accurate 
 than native docking ( which is not true for glide sp ) . furthermore , 
 ensemble docking using glide htvs is also significantly more accurate 
 than cross docking using glide sp . in summary ,
if many protein 
 conformations are available , the docking 
 success rate can be drastically improved by running calculations on 
 a single protein structure selected by means of fingerprint similarity 
 to the respective crystallographic ligand .
this is particularly true 
 for glide sp , as the algorithm performs a significantly more exhaustive 
 ligand conformational search , which leads to a considerable difference 
 in docking success rate compared to glide htvs .
these observations 
 are likely to be reproduced for different targets according to the 
 extent of the ligand - dependent protein conformational change .
box plot describing 
 the distributions of the rmsds of predicted 
 binding modes from crystallographic ligands using different docking 
 approaches and protocols .
the box is delimited by the 25th and the 
 75th percentile values ; the line within the box represents the median . 

data points are defined as outliers ( green dots ) if their distance 
 from the 75th percentile is over 1.5 times the interquartile range 
 ( distance between the 25th and 75th percentile ) .
ultimately , in virtual 
 screening , we would like to discern active compounds from inactives . 

this is likely due to the many , often in controllable , variables 
 affecting the outcome of the analysis : studies should 
 be prospective . molecules similar to the crystallographic ligand will 
 be synthesized after its discovery , including the structurally related 
 compounds in the virtual screening data set that may inherently affect 
 the objectivity of the study .
users performing the studies may be 
 more expert with 
 one method than another .
the options of several methods 
 could be pre - optimized , 
 and this could boost the success rate .
the metrics used 
 for judging the performance of a virtual 
 screening campaign is affected by the characteristics of the data 
 set ( e.g. , ratio of ligands to decoys ) . the
criteria 
 used for selecting the inactives or decoys 
 can affect the enrichment factors ( e.g. , pharmacophoric prescreening , 
 physicochemical similarity , or random ) .
screening success for 
 different virtual screening campaigns using 
 the dud and the gsk data sets .
for the dud data sets , both the auc 
 ( representing the ability to distinguish actives from inactives ) and 
 the bedroc at  = 20 ( representing the ability to associate 
 the highest scores to active molecules ) are considered due to the 
 high ligand - to - decoy ratio .
methods other than  multiple  
 are only aware of either a single protein structure or a single crystallographic 
 ligand . in this study ,
two data sets were 
 used : the gsk data set composed 
 of 294 compounds ( 24 confirmed actives and 270 confirmed inactives 
 versus cdk2 ) and the dud data set composed of 1829 compounds ( 50 confirmed 
 actives against cdk2 , the remaining compounds are decoys ) .
we observed 
 no comparability between the auc ( area under the receiver operator 
 characteristic curve ) in virtual screening using the three different 
 in silico technologies ( ecfp_4 fingerprint similarity , rocs 3d similarity , 
 and glide docking ) applied to the gsk and the dud data sets , suggesting 
 that the same virtual screening strategies applied to different data 
 sets do not generally perform comparably well ( figure 4 ) .
overall , the average auc for 2d methods ( 0.51  0.15 ) 
 was lower than for 3d ligand - based methods ( 0.61  0.15 ) and 
 docking ( 0.71  0.15 ) ; thus , in this case , the accuracy ( quantified 
 as auc ) was directly proportional to the computational expense .
it 
 is interesting to note that other studies comparing different screening 
 technologies ( and using different metrics ) reached completely different 
 conclusions from ours as well as from each other , highlighting the 
 variability associated with different users , data sets , and software .
it is also noteworthy that our data set contained examples of ligands 
 that could be used as templates for ecfp_4 similarity searches yielding 
 higher auc with respect to the rocs 3d similarity search and htvs 
 glide docking on the corresponding protein structure ( e.g. , fingerprint 
 similarity based on pdb 4ez3 produced auc = 0.87 when applied to the gsk data set , 
 see supporting information ) . in summary , 
 starting from a single protein 
ligand complex , there is no 
 way a priori to know which method will perform best . for the dud data 

set , performances are reported both in terms of auc and bedroc at 
  = 20 , which is often reported as a metric suitable for evaluating 
 the  early enrichment  .
from the two plots , the same conclusion can be derived ; using methods 
 aware of multiple protein  ligand complexes consistently increases 
 the chances of virtual screening success rates .
the average between sensitivity and specificity 
 ( ccr ) is reported for the gsk data sets , for which inactives are available . 

the bedroc20 is reported for the dud data set due to the high ligand 
 to decoy ratio .
 ens  is the abbreviation of ensemble , 
 where the best score is selected after screening against all the crystallographic 
 structures available .
htvs rocs_tc and htvs ecfp_4 identify htvs docking 
 experiments based on a protein structure selected by means of ligand - based 
 similarity .
z2 identify data fusion approaches based on ensemble htvs 
 docking , ensemble rocs tanimoto combo , and ensemble ecfp_4 similarity . 

htvs docking on a single protein structure was selected using ecfp_4 
 similarity , ensemble rocs tanimoto combo , and ensemble ecfp_4 .
methods using information from multiple 
 protein  ligand complexes 
 are reported in table 1 . for the gsk data set
, 
 the correct classification rate ( ccr , which is the average between 
 specificity and sensitivity ) is reported due to the availability of 
 experimentally confirmed inactives .
ccr values in table 1 meet all of the requirements typically expected by predictive 
 qsar classification models .
each of the 
 methods in table 1 outputs only one score , 
 which is not weighted or elaborated by any statistical tool , and therefore 
 could be more generalizable with respect to qsar models , which are 
 inevitably influenced by the size of the data set , chemotypes contained 
 in the training set , number of actives versus inactives , and likelihood 
 of overfitting .
ensemble methods in table 1 ( rows 13 ) 
 use the best score across the panel of 40 protein  ligand complexes 
 for each screened molecule . while for ligand - based methods , ensemble 
 scoring
could easily be performed in virtual screening campaigns , 
 for docking the number of processors , licenses and time available 
 will necessarily be limiting factors .
rows 4 and 5 are related to 
 the use of glide htvs docking on a single protein conformation selected 
 by means of ecfp_4 or rocs tanimoto combo similarity to the crystallographic 
 ligand pose .
these methods use the information from every protein 
 complex available , produce binding hypotheses as output , and can be 
 completed in a reasonable computational time .
the last two methods 
 reported in table 1 are based on the data fusion 
 metric z2 , which is derived by the fisher z - transformation 
 of the distributions of each one of the considered scores ( ecfp_4 , 
 rocs tanimoto combo , glide htvs ) .
such transformation provides a unified 
 framework to numerically compare data belonging to the different distributions . 

the caveat in this case is that the sign of the z values associated 
 with glide htvs is inverted , as negative docking values identify better 
 solutions , while for ecfp_4 and rocs scores , the opposite is true . 

z2 is the average of the value of the two highest z - scores 
 among the three considered . in table 1 , z2 values are derived either from ensemble 
 docking ( on the 40 cdk2 protein structures ) or from docking on a single 
 protein conformation selected by means of ecfp_4 tanimoto similarity 
 to the native ligand . for the gsk and the dud data sets , each of the 
 methods in table 1 performs excellently .
the 
 best auc and bedroc values were observed when using all of the available 
 in silico technologies and all of the available protein complexes 
 ( row 7 , table 1 ) .
not surprisingly , this screening 
 strategy is also the most computationally intensive , both in terms 
 of time and software licenses required .
while ligand - based ensemble methods offer the advantage 
 of exploiting a larger amount of experimental information in a reasonable 
 computational time , docking on a selected structure has the advantage 
 of offering binding poses as a result .
the best identified poses can 
 be further optimized using more accurate docking protocols ( e.g. , 
 glide sp ) , visual inspection , torsion analysis , tautomer / protomer 
 analysis , and if a hypothesis is supported by experimental data ( e.g. , 
 crystallography , nmr , sar ) , binding - mode testing .
the latter refinement 
 steps have a considerably lower throughput and a higher degree of 
 chemical knowledge and user input required but could be crucial for 
 further enriching the docking hit selection with experimental hits .
the plots in figure 5 compare the relative number of actives for the 
 two external data 
 sets in different ranges of docking scores and ecfp_4 tanimoto similarity 
 to the native ligand . in this scenario ,
fingerprint similarity serves 
 as a reliability index for docking experiments ; for docking poses 
 with scores better than 8 , the percentage of actives increases 
 with the fingerprint similarity .
while the relative percentage of 
 actives is different in the two data sets , the enrichment trend is 
 the same .
practical implications of these plots are that approximately 
 90% of the screening library could be filtered by means of ecfp_4 
 similarity ( below 0.2 ) , while for the remaining compounds , glide sp 
 docking ( as opposed to htvs ) could be used in a comparable computational 
 time .
while compounds with high fingerprint similarity ( e.g. , greater 
 than 0.4 ) and good docking score ( e.g. , better than 9 ) would 
 most likely be virtual screening hits if a z2 approach 
 were used , there is a significant number of compounds with intermediate 
 ligand similarity ( 0.20.4 ) that would not be identified by 
 data fusion .
these compounds would by definition be different from 
 the native ligand and could therefore bring novelty to medicinal chemistry 
 projects .
the docking success rate for compounds with intermediate 
 similarity to the native ligand is lower with respect to docking applied 
 to compounds with high similarity ; however , this is still significantly 
 higher than random .
depending on the available resources together 
 with the objectives of the medicinal chemistry project , computational 
 and medicinal chemists can choose the extent to which they intend 
 to favor success rate over novelty or vice versa .
relative percentage of 
 actives and decoys ( dud data set , upper 
 plot ) and actives and inactives ( gsk data set , lower plot ) in different 
 ecfp_4 and htvs glide docking score ranges .
many different computational methods are 
 available to modelers . 
 however , the complementarity of these methods and the extent to which 
 they may be combined to improve true positive hit rates has had little 
 exploration . here ,
ligand - based methods ( molecular similarity ) have 
 been applied to provide guidance on probabilities of success of molecular 
 docking strategies .
these experiments provide a framework from which 
 experiments may be designed to incorporate learning from both ligand - based 
 and structure - based modeling methodologies .
the value of the experimental 
 data is maximized , which will result in solutions that will more likely 
 have interpretable hypotheses with the ability to more robustly interpret 
 negative data that may be important for medicinal chemistry design .
it is well documented that it is impossible to know a priori which 
 computational methodology will be most appropriate , in terms of providing 
 true positives , when starting a virtual screening campaign .
the incorporation of multiple protein  ligand complexes , where 
 available , and the application of multiple computational methodologies 
 using data fusion is preferable to using a single protein conformation 
 in a virtual screening approach .
when multiple protein structures 
 are available , we recommend the application of ligand - based methods 
 to select an appropriate protein conformation to maximize the ligand 
 similarity of compound sets to be docked .
this approach will also 
 provide a probable confidence indicator for the compounds to be docked . 
 in particular , cross docking on the protein structure bound to the 
 most similar crystallographic ligand available
significantly increases 
 the success rate of the docking method , regardless of the degree of 
 precision of the algorithm .
although it is important to have 
 a reliable and robust method , 
 to the extent that the methods and data permit , the structural novelty 
 of solutions is also of significant importance .
the applicability 
 domain of any model can tested to its limits in the search for novel 
 hit matter , but caution must be exhibited to ensure that the resulting 
 predictions are appropriate and robust .
the discovery of a single , 
 yet structurally novel , hit ligand can be much more beneficial and 
 informative in the early stages of a drug design project than the 
 identification of many close analogues of a known ligand . according 
 to the results of this study ,
if for a project there 
 is a high number of crystallographic protein  ligand complexes 
 available , data fusion and ensemble docking can provide classification 
 accuracy that is comparable or superior to classification accuracy 
 and auc values identifying predictive qspr models ; for the methods 
 analyzed , the correct classification rate ( average between specificity 
 and sensitivity ) was always greater than 85% ( and the auc was greater 
 than 0.9 ) when predicting a set of 294 experimentally tested cdk2 
 inhibitors and non - inhibitors .
furthermore , these models have the 
 potential to be more generalizable allowing the identification of 
 novel and diverse ligands .
the framework presented here offers 
 an approach to applying ligand 
 docking in drug design projects using the most appropriate co - crystal 
 structure according to ligand similarity .
this leads to higher confidence 
 in docking results using structures with similar ligands but also 
 a more thorough understanding of the confidence of docking results 
 with structures that have less similar ligands .
the results of this 
 study offer a new approach to allowing modelers greater control of 
 the utility of ligand docking in drug design projects .
 virtual 
 screening with docking is an integral component of drug 
 design , particularly during hit finding phases . 
 while successful prospective 
 studies of virtual screening exist , it remains a significant challenge 
 to identify best practices a priori due to the many factors that influence 
 the final outcome , including targets , data sets , software , metrics , 
 and expert knowledge of the users . 
 this study investigates the extent 
 to which ligand - based methods can be applied to improve structure - based 
 methods . 
 the use of ligand - based methods to modulate the number of 
 hits identified using the protein  ligand complex and also the 
 diversity of these hits from the crystallographic ligand is discussed . 
 in this study 
 , 40 cdk2 ligand complexes were used together with two 
 external data sets containing both actives and inactives from glaxosmithkline 
 ( gsk ) and actives and decoys from the directory of useful decoys ( dud ) . 
 
 results show how ligand - based modeling can be used to select a more 
 appropriate protein conformation for docking , as well as to assess 
 the reliability of the docking experiment . the time gained by reducing 
 the pool of virtual screening candidates via ligand - based similarity 
 
 can be invested in more accurate docking procedures , as well as in 
 downstream labor - intensive approaches ( e.g. , visual inspection ) maximizing 
 the use of the chemical and biological information available . 
 this 
 provides a framework for molecular modeling scientists that are involved 
 in initiating virtual screening campaigns with practical advice to 
 make best use of the information available to them . 
Introduction
Material and Methods
Results and Discussion
Conclusions